diff --git a/notebooks/.ipynb_checkpoints/bim_stats-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/bim_stats-checkpoint.ipynb
deleted file mode 100644
index 9a8c80e..0000000
--- a/notebooks/.ipynb_checkpoints/bim_stats-checkpoint.ipynb
+++ /dev/null
@@ -1,1243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "a7daa401",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import io\n",
-    "sns.set(rc={'figure.figsize':(11,8)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "4a8cd87c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Medical Imaging Modality</th>\n",
-       "      <th>Biomed Task</th>\n",
-       "      <th>ML Task</th>\n",
-       "      <th>License</th>\n",
-       "      <th>Performance</th>\n",
-       "      <th>Supervised</th>\n",
-       "      <th>Parameters download</th>\n",
-       "      <th>Reference</th>\n",
-       "      <th>Link to model</th>\n",
-       "      <th>Link to dataset</th>\n",
-       "      <th>Parameters link</th>\n",
-       "      <th>Comment</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3D microscopy</td>\n",
-       "      <td>Nuclei detection</td>\n",
-       "      <td>Segmentation</td>\n",
-       "      <td>MIT</td>\n",
-       "      <td>0.937 AP</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Hirsch, Peter, and Dagmar Kainmueller. \"An aux...</td>\n",
-       "      <td>https://github.com/Kainmueller-Lab/aux_cpv_loss</td>\n",
-       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>dataset comes from private support of another ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Confocal microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>Apache-2.0</td>\n",
-       "      <td>0.87 SSIM</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Vizcaino, Josue Page, et al. \"Learning to Reco...</td>\n",
-       "      <td>https://github.com/pvjosue/LFMNet</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>34.45 PSNR, 0.87 SSIM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>2.5 Reconstruction accuracy</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zhong, E. D., Bepler, T., Davis, J. H., &amp; Berg...</td>\n",
-       "      <td>https://github.com/zhonge/cryodrgn</td>\n",
-       "      <td>https://www.ebi.ac.uk/empiar/EMPIAR-10076/</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>GAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Protein structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>5.82 Angstrom</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...</td>\n",
-       "      <td>https://github.com/KarenUllrich/pytorch-backpr...</td>\n",
-       "      <td>https://github.com/KarenUllrich/Pytorch-Backpr...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Validate the backpropagation with FSC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>Kidney tumor</td>\n",
-       "      <td>Segmentation</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>0.95 Dice</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Müller, D., &amp; Kramer, F. (2021). MIScnn: a fra...</td>\n",
-       "      <td>https://github.com/frankkramer-lab/MIScnn</td>\n",
-       "      <td>https://github.com/neheller/kits19,  https://d...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Dice coefficient median 0.9544 for kidney Segm...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Medical Imaging Modality                         Biomed Task  \\\n",
-       "0             3D microscopy                    Nuclei detection   \n",
-       "1       Confocal microscopy  Cellular structures reconstruction   \n",
-       "2  Cryo-electron microscopy  Cellular structures reconstruction   \n",
-       "3  Cryo-electron microscopy   Protein structures reconstruction   \n",
-       "4                        CT                        Kidney tumor   \n",
-       "\n",
-       "          ML Task     License                  Performance  Supervised  \\\n",
-       "0    Segmentation         MIT                     0.937 AP        True   \n",
-       "1  Reconstruction  Apache-2.0                    0.87 SSIM        True   \n",
-       "2  Reconstruction     GPL-3.0  2.5 Reconstruction accuracy       False   \n",
-       "3  Reconstruction     GPL-3.0                5.82 Angstrom        True   \n",
-       "4    Segmentation     GPL-3.0                    0.95 Dice        True   \n",
-       "\n",
-       "   Parameters download                                          Reference  \\\n",
-       "0                False  Hirsch, Peter, and Dagmar Kainmueller. \"An aux...   \n",
-       "1                False  Vizcaino, Josue Page, et al. \"Learning to Reco...   \n",
-       "2                False  Zhong, E. D., Bepler, T., Davis, J. H., & Berg...   \n",
-       "3                False  Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...   \n",
-       "4                False  Müller, D., & Kramer, F. (2021). MIScnn: a fra...   \n",
-       "\n",
-       "                                       Link to model  \\\n",
-       "0    https://github.com/Kainmueller-Lab/aux_cpv_loss   \n",
-       "1                  https://github.com/pvjosue/LFMNet   \n",
-       "2                 https://github.com/zhonge/cryodrgn   \n",
-       "3  https://github.com/KarenUllrich/pytorch-backpr...   \n",
-       "4          https://github.com/frankkramer-lab/MIScnn   \n",
-       "\n",
-       "                                     Link to dataset Parameters link  \\\n",
-       "0  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...             N/a   \n",
-       "1                                                N/a             N/a   \n",
-       "2         https://www.ebi.ac.uk/empiar/EMPIAR-10076/             N/a   \n",
-       "3  https://github.com/KarenUllrich/Pytorch-Backpr...             N/a   \n",
-       "4  https://github.com/neheller/kits19,  https://d...             N/a   \n",
-       "\n",
-       "                                             Comment  \n",
-       "0  dataset comes from private support of another ...  \n",
-       "1                              34.45 PSNR, 0.87 SSIM  \n",
-       "2                                                GAN  \n",
-       "3              Validate the backpropagation with FSC  \n",
-       "4  Dice coefficient median 0.9544 for kidney Segm...  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv('../data/model_list.csv')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "2be77e34",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_174/2685241325.py:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['Parameters storage'][gdrive.index] = 'Google drive'\n",
-      "/tmp/ipykernel_174/2685241325.py:5: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['Parameters storage'][dbox.index] = 'Dropbox'\n",
-      "/tmp/ipykernel_174/2685241325.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['Parameters storage'][gh.index] = 'GitHub'\n"
-     ]
-    }
-   ],
-   "source": [
-    "df['Parameters storage'] = 'N/a'\n",
-    "gdrive = df.loc[df['Parameters link'].str.contains(\"drive\", case=False, na=False)]\n",
-    "df['Parameters storage'][gdrive.index] = 'Google drive'\n",
-    "dbox = df.loc[df['Parameters link'].str.contains(\"dropbox\", case=False, na=False)]\n",
-    "df['Parameters storage'][dbox.index] = 'Dropbox'\n",
-    "gh = df.loc[df['Parameters link'].str.contains(\"github\", case=False, na=False)]\n",
-    "df['Parameters storage'][gh.index] = 'GitHub'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "793e2146",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Medical Imaging Modality</th>\n",
-       "      <th>Biomed Task</th>\n",
-       "      <th>ML Task</th>\n",
-       "      <th>License</th>\n",
-       "      <th>Performance</th>\n",
-       "      <th>Supervised</th>\n",
-       "      <th>Parameters download</th>\n",
-       "      <th>Reference</th>\n",
-       "      <th>Link to model</th>\n",
-       "      <th>Link to dataset</th>\n",
-       "      <th>Parameters link</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Parameters storage</th>\n",
-       "      <th>Data download</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3D microscopy</td>\n",
-       "      <td>Nuclei detection</td>\n",
-       "      <td>Segmentation</td>\n",
-       "      <td>MIT</td>\n",
-       "      <td>0.937 AP</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Hirsch, Peter, and Dagmar Kainmueller. \"An aux...</td>\n",
-       "      <td>https://github.com/Kainmueller-Lab/aux_cpv_loss</td>\n",
-       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>dataset comes from private support of another ...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Confocal microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>Apache-2.0</td>\n",
-       "      <td>0.87 SSIM</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Vizcaino, Josue Page, et al. \"Learning to Reco...</td>\n",
-       "      <td>https://github.com/pvjosue/LFMNet</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>34.45 PSNR, 0.87 SSIM</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>2.5 Reconstruction accuracy</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zhong, E. D., Bepler, T., Davis, J. H., &amp; Berg...</td>\n",
-       "      <td>https://github.com/zhonge/cryodrgn</td>\n",
-       "      <td>https://www.ebi.ac.uk/empiar/EMPIAR-10076/</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>GAN</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Protein structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>5.82 Angstrom</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...</td>\n",
-       "      <td>https://github.com/KarenUllrich/pytorch-backpr...</td>\n",
-       "      <td>https://github.com/KarenUllrich/Pytorch-Backpr...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Validate the backpropagation with FSC</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>Kidney tumor</td>\n",
-       "      <td>Segmentation</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>0.95 Dice</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Müller, D., &amp; Kramer, F. (2021). MIScnn: a fra...</td>\n",
-       "      <td>https://github.com/frankkramer-lab/MIScnn</td>\n",
-       "      <td>https://github.com/neheller/kits19,  https://d...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Dice coefficient median 0.9544 for kidney Segm...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Medical Imaging Modality                         Biomed Task  \\\n",
-       "0             3D microscopy                    Nuclei detection   \n",
-       "1       Confocal microscopy  Cellular structures reconstruction   \n",
-       "2  Cryo-electron microscopy  Cellular structures reconstruction   \n",
-       "3  Cryo-electron microscopy   Protein structures reconstruction   \n",
-       "4                        CT                        Kidney tumor   \n",
-       "\n",
-       "          ML Task     License                  Performance  Supervised  \\\n",
-       "0    Segmentation         MIT                     0.937 AP        True   \n",
-       "1  Reconstruction  Apache-2.0                    0.87 SSIM        True   \n",
-       "2  Reconstruction     GPL-3.0  2.5 Reconstruction accuracy       False   \n",
-       "3  Reconstruction     GPL-3.0                5.82 Angstrom        True   \n",
-       "4    Segmentation     GPL-3.0                    0.95 Dice        True   \n",
-       "\n",
-       "   Parameters download                                          Reference  \\\n",
-       "0                False  Hirsch, Peter, and Dagmar Kainmueller. \"An aux...   \n",
-       "1                False  Vizcaino, Josue Page, et al. \"Learning to Reco...   \n",
-       "2                False  Zhong, E. D., Bepler, T., Davis, J. H., & Berg...   \n",
-       "3                False  Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...   \n",
-       "4                False  Müller, D., & Kramer, F. (2021). MIScnn: a fra...   \n",
-       "\n",
-       "                                       Link to model  \\\n",
-       "0    https://github.com/Kainmueller-Lab/aux_cpv_loss   \n",
-       "1                  https://github.com/pvjosue/LFMNet   \n",
-       "2                 https://github.com/zhonge/cryodrgn   \n",
-       "3  https://github.com/KarenUllrich/pytorch-backpr...   \n",
-       "4          https://github.com/frankkramer-lab/MIScnn   \n",
-       "\n",
-       "                                     Link to dataset Parameters link  \\\n",
-       "0  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...             N/a   \n",
-       "1                                                N/a             N/a   \n",
-       "2         https://www.ebi.ac.uk/empiar/EMPIAR-10076/             N/a   \n",
-       "3  https://github.com/KarenUllrich/Pytorch-Backpr...             N/a   \n",
-       "4  https://github.com/neheller/kits19,  https://d...             N/a   \n",
-       "\n",
-       "                                             Comment Parameters storage  \\\n",
-       "0  dataset comes from private support of another ...                N/a   \n",
-       "1                              34.45 PSNR, 0.87 SSIM                N/a   \n",
-       "2                                                GAN                N/a   \n",
-       "3              Validate the backpropagation with FSC                N/a   \n",
-       "4  Dice coefficient median 0.9544 for kidney Segm...                N/a   \n",
-       "\n",
-       "   Data download  \n",
-       "0           True  \n",
-       "1          False  \n",
-       "2           True  \n",
-       "3           True  \n",
-       "4           True  "
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Data download'] = False\n",
-    "df.loc[df['Link to dataset']!= 'N/a', 'Data download'] = True\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "5c2c72b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'License'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "33fe04e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Data download'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "ddb898fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Parameters download'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "ca3c5e6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Supervised'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "2da3b5ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'ML Task'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "7406d97a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Biomed Task'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "cd5810a7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Medical Imaging Modality'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df[col].unique())]\n",
-    "data = df[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "9af23b7f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Medical Imaging Modality</th>\n",
-       "      <th>Biomed Task</th>\n",
-       "      <th>ML Task</th>\n",
-       "      <th>License</th>\n",
-       "      <th>Performance</th>\n",
-       "      <th>Supervised</th>\n",
-       "      <th>Parameters download</th>\n",
-       "      <th>Reference</th>\n",
-       "      <th>Link to model</th>\n",
-       "      <th>Link to dataset</th>\n",
-       "      <th>Parameters link</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Parameters storage</th>\n",
-       "      <th>Data download</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>Pulmonary nodule</td>\n",
-       "      <td>Segmentation</td>\n",
-       "      <td>CC BY-NC-SA 4.0</td>\n",
-       "      <td>0.55 IoU</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Aresta, G., Jacobs, C., Araújo, T., Cunha, A.,...</td>\n",
-       "      <td>https://github.com/gmaresta/iW-Net</td>\n",
-       "      <td>https://wiki.cancerimagingarchive.net/display/...</td>\n",
-       "      <td>https://github.com/gmaresta/iW-Net/blob/master...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>GitHub</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Dermatoscopy</td>\n",
-       "      <td>Skin tumor</td>\n",
-       "      <td>Classification</td>\n",
-       "      <td>MIT</td>\n",
-       "      <td>0.81 AUC</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Zunair, H., &amp; Hamza, A. B. (2020). Melanoma de...</td>\n",
-       "      <td>https://github.com/hasibzunair/adversarial-les...</td>\n",
-       "      <td>https://challenge.isic-archive.com/data</td>\n",
-       "      <td>https://github.com/hasibzunair/adversarial-les...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>GitHub</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Mammography</td>\n",
-       "      <td>Breast tumor</td>\n",
-       "      <td>Classification</td>\n",
-       "      <td>CC BY-NC-ND 4.0</td>\n",
-       "      <td>0.93 AUC</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Shen, Y., Wu, N., Phang, J., Park, J., Liu, K....</td>\n",
-       "      <td>https://github.com/nyukat/GMIC</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>https://github.com/nyukat/GMIC/tree/master/models</td>\n",
-       "      <td>Weakly supervised</td>\n",
-       "      <td>GitHub</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Mammography</td>\n",
-       "      <td>Breast tumor</td>\n",
-       "      <td>Classification</td>\n",
-       "      <td>CC BY-NC-SA 4.0</td>\n",
-       "      <td>0.84 Accuracy</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Shen, L., Margolies, L. R., Rothstein, J. H., ...</td>\n",
-       "      <td>https://github.com/lishen/end2end-all-conv</td>\n",
-       "      <td>https://www.kaggle.com/datasets/awsaf49/cbis-d...</td>\n",
-       "      <td>https://drive.google.com/drive/folders/0B1PVLa...</td>\n",
-       "      <td>83.5% accuracy for single Model AUC, 86% accur...</td>\n",
-       "      <td>Google drive</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Digital Pathology</td>\n",
-       "      <td>Breast tumor</td>\n",
-       "      <td>Classification</td>\n",
-       "      <td>MIT</td>\n",
-       "      <td>0.93 Accuracy</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Rakhlin, A., Shvets, A., Iglovikov, V., &amp; Kali...</td>\n",
-       "      <td>https://github.com/alexander-rakhlin/ICIAR2018</td>\n",
-       "      <td>https://rdm.inesctec.pt/dataset/nis-2017-003</td>\n",
-       "      <td>https://www.dropbox.com/s/oklwgz5v9suyj1k/ICIA...</td>\n",
-       "      <td>93.8% accuracy, AUC 97.3%, and sensitivity/spe...</td>\n",
-       "      <td>Dropbox</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Medical Imaging Modality       Biomed Task         ML Task  \\\n",
-       "11                       CT  Pulmonary nodule    Segmentation   \n",
-       "18             Dermatoscopy       Skin tumor   Classification   \n",
-       "22              Mammography      Breast tumor  Classification   \n",
-       "24              Mammography      Breast tumor  Classification   \n",
-       "25        Digital Pathology      Breast tumor  Classification   \n",
-       "\n",
-       "            License    Performance  Supervised  Parameters download  \\\n",
-       "11  CC BY-NC-SA 4.0       0.55 IoU        True                 True   \n",
-       "18              MIT       0.81 AUC        True                 True   \n",
-       "22  CC BY-NC-ND 4.0       0.93 AUC        True                 True   \n",
-       "24  CC BY-NC-SA 4.0  0.84 Accuracy        True                 True   \n",
-       "25              MIT  0.93 Accuracy        True                 True   \n",
-       "\n",
-       "                                            Reference  \\\n",
-       "11  Aresta, G., Jacobs, C., Araújo, T., Cunha, A.,...   \n",
-       "18  Zunair, H., & Hamza, A. B. (2020). Melanoma de...   \n",
-       "22  Shen, Y., Wu, N., Phang, J., Park, J., Liu, K....   \n",
-       "24  Shen, L., Margolies, L. R., Rothstein, J. H., ...   \n",
-       "25  Rakhlin, A., Shvets, A., Iglovikov, V., & Kali...   \n",
-       "\n",
-       "                                        Link to model  \\\n",
-       "11                 https://github.com/gmaresta/iW-Net   \n",
-       "18  https://github.com/hasibzunair/adversarial-les...   \n",
-       "22                     https://github.com/nyukat/GMIC   \n",
-       "24         https://github.com/lishen/end2end-all-conv   \n",
-       "25     https://github.com/alexander-rakhlin/ICIAR2018   \n",
-       "\n",
-       "                                      Link to dataset  \\\n",
-       "11  https://wiki.cancerimagingarchive.net/display/...   \n",
-       "18            https://challenge.isic-archive.com/data   \n",
-       "22                                                N/a   \n",
-       "24  https://www.kaggle.com/datasets/awsaf49/cbis-d...   \n",
-       "25       https://rdm.inesctec.pt/dataset/nis-2017-003   \n",
-       "\n",
-       "                                      Parameters link  \\\n",
-       "11  https://github.com/gmaresta/iW-Net/blob/master...   \n",
-       "18  https://github.com/hasibzunair/adversarial-les...   \n",
-       "22  https://github.com/nyukat/GMIC/tree/master/models   \n",
-       "24  https://drive.google.com/drive/folders/0B1PVLa...   \n",
-       "25  https://www.dropbox.com/s/oklwgz5v9suyj1k/ICIA...   \n",
-       "\n",
-       "                                              Comment Parameters storage  \\\n",
-       "11                                                NaN             GitHub   \n",
-       "18                                                NaN             GitHub   \n",
-       "22                                  Weakly supervised             GitHub   \n",
-       "24  83.5% accuracy for single Model AUC, 86% accur...       Google drive   \n",
-       "25  93.8% accuracy, AUC 97.3%, and sensitivity/spe...            Dropbox   \n",
-       "\n",
-       "    Data download  \n",
-       "11           True  \n",
-       "18           True  \n",
-       "22          False  \n",
-       "24           True  \n",
-       "25           True  "
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_p = df.loc[df['Parameters download']==True]\n",
-    "df_p.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "9fb5b084",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "col = 'Parameters storage'\n",
-    "colors = sns.color_palette('colorblind')[0:len(df_p[col].unique())]\n",
-    "data = df_p[col].value_counts()\n",
-    "\n",
-    "plt.pie(data, colors=colors, autopct='%.0f%%')\n",
-    "plt.legend(df[col].unique(), loc=\"best\")\n",
-    "plt.savefig(f'../figs/{col}.svg', format='svg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "7d728649",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Medical Imaging Modality</th>\n",
-       "      <th>Biomed Task</th>\n",
-       "      <th>ML Task</th>\n",
-       "      <th>License</th>\n",
-       "      <th>Performance</th>\n",
-       "      <th>Supervised</th>\n",
-       "      <th>Parameters download</th>\n",
-       "      <th>Reference</th>\n",
-       "      <th>Link to model</th>\n",
-       "      <th>Link to dataset</th>\n",
-       "      <th>Parameters link</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Parameters storage</th>\n",
-       "      <th>Data download</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Confocal microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>Apache-2.0</td>\n",
-       "      <td>0.87 SSIM</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Vizcaino, Josue Page, et al. \"Learning to Reco...</td>\n",
-       "      <td>https://github.com/pvjosue/LFMNet</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>34.45 PSNR, 0.87 SSIM</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>2.5 Reconstruction accuracy</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zhong, E. D., Bepler, T., Davis, J. H., &amp; Berg...</td>\n",
-       "      <td>https://github.com/zhonge/cryodrgn</td>\n",
-       "      <td>https://www.ebi.ac.uk/empiar/EMPIAR-10076/</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>GAN</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Cryo-electron microscopy</td>\n",
-       "      <td>Protein structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>GPL-3.0</td>\n",
-       "      <td>5.82 Angstrom</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...</td>\n",
-       "      <td>https://github.com/KarenUllrich/pytorch-backpr...</td>\n",
-       "      <td>https://github.com/KarenUllrich/Pytorch-Backpr...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Validate the backpropagation with FSC</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>Pulmonary nodule</td>\n",
-       "      <td>Detection</td>\n",
-       "      <td>Apache-2.0</td>\n",
-       "      <td>0.76 FROC AUC</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zhu, W., Vang, Y. S., Huang, Y., &amp; Xie, X. (20...</td>\n",
-       "      <td>https://github.com/uci-cbcl/DeepEM-for-Weakly-...</td>\n",
-       "      <td>https://biometry.nci.nih.gov/cdas/nlst/ , http...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Average FROC 76% for DeepEM(Map) and 76.4% for...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>Pulmonary nodule</td>\n",
-       "      <td>Detection</td>\n",
-       "      <td>MIT</td>\n",
-       "      <td>0.86 FROC AUC</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Li, Y., &amp; Fan, Y. (2020, April). DeepSEED: 3D ...</td>\n",
-       "      <td>https://github.com/ymli39/DeepSEED-3D-ConvNets...</td>\n",
-       "      <td>https://luna16.grand-challenge.org/data , http...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>FROC 86.2% on LUNA16 dataset and 77.3% on LIDC...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Mammography</td>\n",
-       "      <td>Breast tumor</td>\n",
-       "      <td>Image Inpainting &amp; Data generation</td>\n",
-       "      <td>CC BY-NC-ND 4.0</td>\n",
-       "      <td>0.896 AUC</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Wu, E., Wu, K., Cox, D., &amp; Lotter, W. (2018). ...</td>\n",
-       "      <td>https://github.com/ericwu09/mammo-cigan</td>\n",
-       "      <td>https://wiki.cancerimagingarchive.net/display/...</td>\n",
-       "      <td>https://github.com/CQFIO/PhotographicImageSynt...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>GitHub</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Digital Pathology</td>\n",
-       "      <td>Multiple pathologies</td>\n",
-       "      <td>Graph embedding</td>\n",
-       "      <td>AGPL 3.0</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Jaume, G., Pati, P., Anklin, V., Foncubierta, ...</td>\n",
-       "      <td>https://github.com/histocartography/histocarto...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>Plug and play modules</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>Electron microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>0.446 IoU</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Guay, M. D., Emam, Z. A., Anderson, A. B., Aro...</td>\n",
-       "      <td>https://github.com/leapmanlab/examples</td>\n",
-       "      <td>https://leapmanlab.github.io/dense-cell/</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>private dataset from NIBIB, NIH</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Fluorescence microscopy</td>\n",
-       "      <td>Cellular structures reconstruction</td>\n",
-       "      <td>Image Inpainting &amp; Data generation</td>\n",
-       "      <td>Apache-2.0</td>\n",
-       "      <td>0.71 Quality Score</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Eschweiler, D., Rethwisch, M., Jarchow, M., Ko...</td>\n",
-       "      <td>https://github.com/stegmaierj/CellSynthesis</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>GAN for data generation in 3D microscopy; Data...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>Mammography</td>\n",
-       "      <td>Breast tumor</td>\n",
-       "      <td>Classification &amp; Detection</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>0.95 AUC</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Ribli, D., Horváth, A., Unger, Z., Pollner, P....</td>\n",
-       "      <td>https://github.com/riblidezso/frcnn_cad</td>\n",
-       "      <td>http://marathon.csee.usf.edu/Mammography/Datab...</td>\n",
-       "      <td>http://dkrib.web.elte.hu/cad_faster_rcnn/vgg16...</td>\n",
-       "      <td>Two of the three datasets available on request...</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>3D microscopy</td>\n",
-       "      <td>Image acquisition</td>\n",
-       "      <td>Reconstruction</td>\n",
-       "      <td>BSD-3-Clause</td>\n",
-       "      <td>30x Speed</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Saha, D., Schmidt, U., Zhang, Q., Barbotin, A....</td>\n",
-       "      <td>https://github.com/mpicbg-csbd/phasenet</td>\n",
-       "      <td>https://github.com/mpicbg-csbd/phasenet</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>N/a</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Medical Imaging Modality                         Biomed Task  \\\n",
-       "1        Confocal microscopy  Cellular structures reconstruction   \n",
-       "2   Cryo-electron microscopy  Cellular structures reconstruction   \n",
-       "3   Cryo-electron microscopy   Protein structures reconstruction   \n",
-       "14                        CT                    Pulmonary nodule   \n",
-       "15                        CT                    Pulmonary nodule   \n",
-       "26               Mammography                        Breast tumor   \n",
-       "31         Digital Pathology                Multiple pathologies   \n",
-       "34       Electron microscopy  Cellular structures reconstruction   \n",
-       "35   Fluorescence microscopy  Cellular structures reconstruction   \n",
-       "39               Mammography                        Breast tumor   \n",
-       "47             3D microscopy                   Image acquisition   \n",
-       "\n",
-       "                               ML Task          License  \\\n",
-       "1                       Reconstruction       Apache-2.0   \n",
-       "2                       Reconstruction          GPL-3.0   \n",
-       "3                       Reconstruction          GPL-3.0   \n",
-       "14                           Detection       Apache-2.0   \n",
-       "15                           Detection              MIT   \n",
-       "26  Image Inpainting & Data generation  CC BY-NC-ND 4.0   \n",
-       "31                     Graph embedding         AGPL 3.0   \n",
-       "34                      Reconstruction              N/a   \n",
-       "35  Image Inpainting & Data generation       Apache-2.0   \n",
-       "39          Classification & Detection              N/a   \n",
-       "47                      Reconstruction     BSD-3-Clause   \n",
-       "\n",
-       "                    Performance  Supervised  Parameters download  \\\n",
-       "1                     0.87 SSIM        True                False   \n",
-       "2   2.5 Reconstruction accuracy       False                False   \n",
-       "3                 5.82 Angstrom        True                False   \n",
-       "14                0.76 FROC AUC        True                False   \n",
-       "15                0.86 FROC AUC        True                False   \n",
-       "26                    0.896 AUC       False                 True   \n",
-       "31                          N/a       False                False   \n",
-       "34                    0.446 IoU        True                False   \n",
-       "35           0.71 Quality Score       False                False   \n",
-       "39                     0.95 AUC        True                 True   \n",
-       "47                    30x Speed        True                False   \n",
-       "\n",
-       "                                            Reference  \\\n",
-       "1   Vizcaino, Josue Page, et al. \"Learning to Reco...   \n",
-       "2   Zhong, E. D., Bepler, T., Davis, J. H., & Berg...   \n",
-       "3   Ullrich, K., Berg, R. V. D., Brubaker, M., Fle...   \n",
-       "14  Zhu, W., Vang, Y. S., Huang, Y., & Xie, X. (20...   \n",
-       "15  Li, Y., & Fan, Y. (2020, April). DeepSEED: 3D ...   \n",
-       "26  Wu, E., Wu, K., Cox, D., & Lotter, W. (2018). ...   \n",
-       "31  Jaume, G., Pati, P., Anklin, V., Foncubierta, ...   \n",
-       "34  Guay, M. D., Emam, Z. A., Anderson, A. B., Aro...   \n",
-       "35  Eschweiler, D., Rethwisch, M., Jarchow, M., Ko...   \n",
-       "39  Ribli, D., Horváth, A., Unger, Z., Pollner, P....   \n",
-       "47  Saha, D., Schmidt, U., Zhang, Q., Barbotin, A....   \n",
-       "\n",
-       "                                        Link to model  \\\n",
-       "1                   https://github.com/pvjosue/LFMNet   \n",
-       "2                  https://github.com/zhonge/cryodrgn   \n",
-       "3   https://github.com/KarenUllrich/pytorch-backpr...   \n",
-       "14  https://github.com/uci-cbcl/DeepEM-for-Weakly-...   \n",
-       "15  https://github.com/ymli39/DeepSEED-3D-ConvNets...   \n",
-       "26            https://github.com/ericwu09/mammo-cigan   \n",
-       "31  https://github.com/histocartography/histocarto...   \n",
-       "34             https://github.com/leapmanlab/examples   \n",
-       "35        https://github.com/stegmaierj/CellSynthesis   \n",
-       "39            https://github.com/riblidezso/frcnn_cad   \n",
-       "47            https://github.com/mpicbg-csbd/phasenet   \n",
-       "\n",
-       "                                      Link to dataset  \\\n",
-       "1                                                 N/a   \n",
-       "2          https://www.ebi.ac.uk/empiar/EMPIAR-10076/   \n",
-       "3   https://github.com/KarenUllrich/Pytorch-Backpr...   \n",
-       "14  https://biometry.nci.nih.gov/cdas/nlst/ , http...   \n",
-       "15  https://luna16.grand-challenge.org/data , http...   \n",
-       "26  https://wiki.cancerimagingarchive.net/display/...   \n",
-       "31                                                N/a   \n",
-       "34           https://leapmanlab.github.io/dense-cell/   \n",
-       "35                                                NaN   \n",
-       "39  http://marathon.csee.usf.edu/Mammography/Datab...   \n",
-       "47            https://github.com/mpicbg-csbd/phasenet   \n",
-       "\n",
-       "                                      Parameters link  \\\n",
-       "1                                                 N/a   \n",
-       "2                                                 N/a   \n",
-       "3                                                 N/a   \n",
-       "14                                                N/a   \n",
-       "15                                                N/a   \n",
-       "26  https://github.com/CQFIO/PhotographicImageSynt...   \n",
-       "31                                                N/a   \n",
-       "34                                                N/a   \n",
-       "35                                                N/a   \n",
-       "39  http://dkrib.web.elte.hu/cad_faster_rcnn/vgg16...   \n",
-       "47                                                N/a   \n",
-       "\n",
-       "                                              Comment Parameters storage  \\\n",
-       "1                               34.45 PSNR, 0.87 SSIM                N/a   \n",
-       "2                                                 GAN                N/a   \n",
-       "3               Validate the backpropagation with FSC                N/a   \n",
-       "14  Average FROC 76% for DeepEM(Map) and 76.4% for...                N/a   \n",
-       "15  FROC 86.2% on LUNA16 dataset and 77.3% on LIDC...                N/a   \n",
-       "26                                                NaN             GitHub   \n",
-       "31                              Plug and play modules                N/a   \n",
-       "34                    private dataset from NIBIB, NIH                N/a   \n",
-       "35  GAN for data generation in 3D microscopy; Data...                N/a   \n",
-       "39  Two of the three datasets available on request...                N/a   \n",
-       "47                                                NaN                N/a   \n",
-       "\n",
-       "    Data download  \n",
-       "1           False  \n",
-       "2            True  \n",
-       "3            True  \n",
-       "14           True  \n",
-       "15           True  \n",
-       "26           True  \n",
-       "31          False  \n",
-       "34           True  \n",
-       "35           True  \n",
-       "39           True  \n",
-       "47           True  "
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[(df['ML Task']!='Segmentation')&(df['ML Task']!='Classification')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5137d920",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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