diff --git "a/temp.ipynb" "b/temp.ipynb"
deleted file mode 100644--- "a/temp.ipynb"
+++ /dev/null
@@ -1,939 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cec738fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "If you have questions or suggestions, feel free to open an issue at https://github.com/DIAGNijmegen/picai_prep\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import argparse\n",
-    "import os\n",
-    "import shutil\n",
-    "import time\n",
-    "import yaml\n",
-    "import sys\n",
-    "import gdown\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.distributed as dist\n",
-    "import torch.multiprocessing as mp\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "from monai.config import KeysCollection\n",
-    "from monai.metrics import Cumulative, CumulativeAverage\n",
-    "from monai.networks.nets import milmodel, resnet, MILModel\n",
-    "\n",
-    "from sklearn.metrics import cohen_kappa_score\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from torch.utils.data.dataloader import default_collate\n",
-    "from torchvision.models.resnet import ResNet50_Weights\n",
-    "import shutil\n",
-    "from pathlib import Path\n",
-    "from torch.utils.data.distributed import DistributedSampler\n",
-    "from torch.utils.tensorboard import SummaryWriter\n",
-    "from monai.utils import set_determinism\n",
-    "import matplotlib.pyplot as plt\n",
-    "import wandb\n",
-    "import math\n",
-    "import logging\n",
-    "from pathlib import Path\n",
-    "\n",
-    "\n",
-    "from src.model.MIL import MILModel_3D\n",
-    "from src.model.csPCa_model import csPCa_Model\n",
-    "from src.data.data_loader import get_dataloader\n",
-    "from src.utils import save_cspca_checkpoint, get_metrics, setup_logging, save_pirads_checkpoint\n",
-    "from src.train import train_cspca, train_pirads\n",
-    "import SimpleITK as sitk \n",
-    "\n",
-    "import nrrd\n",
-    "\n",
-    "from tqdm import tqdm\n",
-    "import pandas as pd\n",
-    "from picai_prep.preprocessing import PreprocessingSettings, Sample\n",
-    "import multiprocessing\n",
-    "import sys\n",
-    "from src.preprocessing.register_and_crop import register_files\n",
-    "from src.preprocessing.prostate_mask import get_segmask\n",
-    "from src.preprocessing.histogram_match import histmatch\n",
-    "from src.preprocessing.generate_heatmap import get_heatmap\n",
-    "import logging\n",
-    "from pathlib import Path\n",
-    "from src.utils import setup_logging\n",
-    "from src.utils import validate_steps\n",
-    "import argparse\n",
-    "import yaml \n",
-    "from src.data.data_loader import data_transform, list_data_collate\n",
-    "from monai.data import Dataset, load_decathlon_datalist, ITKReader, NumpyReader, PersistentDataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bc433898",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import sys\n",
-    "from pathlib import Path\n",
-    "import torch\n",
-    "import pytest\n",
-    "import argparse\n",
-    "from src.train.train_pirads import get_attention_scores\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f1c90aff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 2\n",
-    "num_patches = 4\n",
-    "\n",
-    "# Sample 0: Target = 3 (Cancer), Sample 1: Target = 0 (PI-RADS 2)\n",
-    "data = torch.randn(batch_size, num_patches, 1, 8, 8)\n",
-    "target = torch.tensor([3.0, 0.0])\n",
-    "\n",
-    "# Create heatmaps: Sample 0 has one \"hot\" patch\n",
-    "heatmap = torch.zeros(batch_size, num_patches, 1, 8, 8)\n",
-    "heatmap[0, 0] = 10.0 # High attention on patch 0 for the first sample\n",
-    "heatmap[0, 3] = 2.0 \n",
-    "heatmap[1, 2] = 5.0  # Should be overridden by PI-RADS 2 logic anyway\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "80cb444f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mock_args():\n",
-    "    # Mocking argparse for the device\n",
-    "    args = argparse.Namespace()\n",
-    "    args.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "    return args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "6528fd4d",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[41], line 23\u001b[0m\n\u001b[1;32m     21\u001b[0m idx \u001b[38;5;241m=\u001b[39m (shuffled_images[\u001b[38;5;241m0\u001b[39m, :, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m5.0\u001b[39m)\u001b[38;5;241m.\u001b[39mnonzero(as_tuple\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# The attention score at that same index should be the maximum\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m att_labels[\u001b[38;5;241m0\u001b[39m, idx] \u001b[38;5;241m==\u001b[39m att_labels[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian()\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "num_patches = 10\n",
-    "\n",
-    "# Distinct data per patch: [0, 1, 2, 3...]\n",
-    "data = torch.arange(num_patches).view(1, num_patches, 1, 1, 1).float()\n",
-    "target = torch.tensor([3.0])\n",
-    "\n",
-    "# Heatmap matches the data indices so we can track the \"label\"\n",
-    "heatmap = torch.arange(num_patches).view(1, num_patches, 1, 1, 1).float()\n",
-    "\n",
-    "att_labels, shuffled_images = get_attention_scores(data, target, heatmap, mock_args)\n",
-    "\n",
-    "\n",
-    "idx= (shuffled_images[0, :, 0, 0, 0] == 9.0).nonzero(as_tuple=True)[0]\n",
-    "# The attention score at that same index should be the maximum\n",
-    "assert att_labels[0, idx] == att_labels[0].max()\n",
-    "\n",
-    "idx = (shuffled_images[0, :, 0, 0, 0] == 0.0).nonzero(as_tuple=True)[0]\n",
-    "# The attention score at that same index should be the maximum\n",
-    "assert att_labels[0, idx] == att_labels[0].min()\n",
-    "\n",
-    "idx = (shuffled_images[0, :, 0, 0, 0] == 5.0).nonzero(as_tuple=True)[0]\n",
-    "# The attention score at that same index should be the maximum\n",
-    "assert att_labels[0, idx] == att_labels[0].median()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "90f5acab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import sys\n",
-    "from pathlib import Path\n",
-    "import torch\n",
-    "import pytest\n",
-    "import argparse\n",
-    "from src.train.train_pirads import get_attention_scores\n",
-    "import monai\n",
-    "from monai.transforms import Transform\n",
-    "from src.data.custom_transforms import NormalizeIntensity_custom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e3a2dc6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = torch.zeros((2, 4, 4), dtype=torch.float32)\n",
-    "mask = torch.zeros((1, 4, 4), dtype=torch.float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "98a500df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.]],\n",
-       "\n",
-       "        [[0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.],\n",
-       "         [0., 0., 0., 0.]]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "img"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c9974f43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img[0, :, :] = 100.0  # Background\n",
-    "img[0, 0, 0] = 10.0   # Masked pixel 1\n",
-    "img[0, 0, 1] = 20.0   # Masked pixel 2\n",
-    "\n",
-    "# --- Channel 1 Setup ---\n",
-    "# Inside mask: Values [2, 4]\n",
-    "# Outside mask: Value 50\n",
-    "img[1, :, :] = 50.0   # Background\n",
-    "img[1, 0, 0] = 2.0    # Masked pixel 1\n",
-    "img[1, 0, 1] = 4.0    # Masked pixel 2\n",
-    "\n",
-    "# --- Mask Setup ---\n",
-    "# Selects only the top-left two pixels (0,0) and (0,1)\n",
-    "mask[0, 0, 0] = 1\n",
-    "mask[0, 0, 1] = 1\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eb910fda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = torch.rand(1, 10, 10)\n",
-    "mask = torch.randint(0, 2, (1, 10, 10)).float()\n",
-    "normalizer = NormalizeIntensity_custom(nonzero=False, channel_wise=True)\n",
-    "out = normalizer(data, mask)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "923341a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "masked = data[mask != 0]\n",
-    "mean_ = torch.mean(masked.float())\n",
-    "std_ = torch.std(masked.float(), unbiased=False)\n",
-    "\n",
-    "epsilon = 1e-8\n",
-    "normalized_data = (data - mean_) / (std_ + epsilon)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "e844cde1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([ 1.4106, -0.1975,  0.3907,  1.2870, -0.7974, -1.2061,  0.7028,  1.2778,\n",
-       "         0.4667, -0.3361, -0.7842, -1.6296, -1.2037,  1.3582, -0.5648, -0.3055,\n",
-       "        -0.3313,  0.0328, -1.0675,  0.6328, -0.2215, -1.3372,  0.5165,  1.9302,\n",
-       "         0.8875,  0.6793,  0.5553,  0.4335,  0.6390, -1.3707,  1.6053,  1.8626,\n",
-       "        -0.3923,  0.2319,  0.3911, -0.4683, -1.1255, -1.6464, -0.2123, -0.5415,\n",
-       "         0.1401, -0.2822,  1.5019, -0.5117, -1.6047, -0.2322, -1.3080,  0.0130,\n",
-       "         1.8028,  0.5602, -1.6317])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "masked"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "a9a20f58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.testing.assert_close(out, normalized_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cad4a637",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import shutil\n",
-    "import json\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bea10ddb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'image': '10270_1000274.nrrd',\n",
-       "  'mask': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/prostate_seg_mask/10270_1000274.nrrd',\n",
-       "  'dwi': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/DWI_hist_matched/10270_1000274.nrrd',\n",
-       "  'adc': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/ADC_hist_matched/10270_1000274.nrrd',\n",
-       "  'heatmap': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/heatmap/10270_1000274.nrrd',\n",
-       "  'label': 0},\n",
-       " {'image': '11063_1001085.nrrd',\n",
-       "  'mask': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/prostate_seg_mask/11063_1001085.nrrd',\n",
-       "  'dwi': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/DWI_hist_matched/11063_1001085.nrrd',\n",
-       "  'adc': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/ADC_hist_matched/11063_1001085.nrrd',\n",
-       "  'heatmap': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/heatmap/11063_1001085.nrrd',\n",
-       "  'label': 1},\n",
-       " {'image': '11184_1001207.nrrd',\n",
-       "  'mask': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/prostate_seg_mask/11184_1001207.nrrd',\n",
-       "  'dwi': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/DWI_hist_matched/11184_1001207.nrrd',\n",
-       "  'adc': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/ADC_hist_matched/11184_1001207.nrrd',\n",
-       "  'heatmap': '/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/registered/heatmap/11184_1001207.nrrd',\n",
-       "  'label': 0}]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with open('/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PICAI_cspca.json', 'r') as f:\n",
-    "    data = json.load(f)\n",
-    "samples = random.sample(data['test'],3)\n",
-    "samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aa932307",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sam \u001b[38;5;241m=\u001b[39m \u001b[43msamples\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03mshutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/t2_images/'+sam, 'dataset/samples/sample3/t2.nrrd')\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03mshutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/DWI_images/'+sam, 'dataset/samples/sample3/dwi.nrrd')\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;124;03mshutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/ADC_images/'+sam, 'dataset/samples/sample3/adc.nrrd')\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "sam = samples[3]['image']\n",
-    "'''\n",
-    "shutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/t2_images/'+sam, 'dataset/samples/sample3/t2.nrrd')\n",
-    "shutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/DWI_images/'+sam, 'dataset/samples/sample3/dwi.nrrd')\n",
-    "shutil.copy('/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/ADC_images/'+sam, 'dataset/samples/sample3/adc.nrrd')\n",
-    "'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c91a5802",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1/1 [00:02<00:00,  2.45s/it]\n",
-      "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "100%|██████████| 1/1 [00:38<00:00, 38.82s/it]\n",
-      "100%|██████████| 1/1 [00:00<00:00,  4.11it/s]\n",
-      "100%|██████████| 1/1 [00:00<00:00,  9.82it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "args = argparse.Namespace(\n",
-    "\n",
-    "    margin = 0.2,\n",
-    "    t2_dir = '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/t2',\n",
-    "    dwi_dir = '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/dwi',\n",
-    "    adc_dir = '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/adc',\n",
-    "    output_dir = '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed',\n",
-    "    project_dir = '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate'\n",
-    ")\n",
-    "\n",
-    "args = register_files(args)\n",
-    "args = get_segmask(args)\n",
-    "args = histmatch(args)\n",
-    "args = get_heatmap(args)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "626227ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Namespace(margin=0.2, t2_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/t2_registered', dwi_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/DWI_registered', adc_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/ADC_registered', output_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed', project_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate', seg_dir='/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/prostate_mask')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f0b80b69",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "bda01625",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = os.listdir(args.t2_dir)\n",
-    "file = files[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "31a977cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t2, t2_spc = nrrd.read(os.path.join(args.t2_dir, file))\n",
-    "dwi, dwi_spc = nrrd.read(os.path.join(args.dwi_dir, file))\n",
-    "adc, adc_spc = nrrd.read(os.path.join(args.adc_dir, file))\n",
-    "seg, seg_spc = nrrd.read(os.path.join(args.seg_dir, file))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3337c138",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(270, 270, 28)\n",
-      "(270, 270, 28)\n",
-      "(270, 270, 28)\n",
-      "(270, 270, 28)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(t2.shape)\n",
-    "print(dwi.shape)\n",
-    "print(adc.shape)\n",
-    "print(seg.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "90c91203",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('type', 'unsigned short'),\n",
-       "             ('dimension', 3),\n",
-       "             ('space', 'left-posterior-superior'),\n",
-       "             ('sizes', array([270, 270,  28])),\n",
-       "             ('space directions',\n",
-       "              array([[ 4.00000000e-01,  1.86604914e-12,  5.95615684e-13],\n",
-       "                     [-1.95880000e-12,  3.81064593e-01,  1.21613224e-01],\n",
-       "                     [-5.96149371e-16, -9.12099183e-01,  2.85798444e+00]])),\n",
-       "             ('kinds', ['domain', 'domain', 'domain']),\n",
-       "             ('endian', 'little'),\n",
-       "             ('encoding', 'raw'),\n",
-       "             ('space origin',\n",
-       "              array([-66.02582741,  -2.30053151, -81.77319682]))])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t2_spc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ff79f6c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('type', 'unsigned short'),\n",
-       "             ('dimension', 3),\n",
-       "             ('space', 'left-posterior-superior'),\n",
-       "             ('sizes', array([270, 270,  28])),\n",
-       "             ('space directions',\n",
-       "              array([[ 4.00000000e-01,  1.86604914e-12,  5.95615684e-13],\n",
-       "                     [-1.95880000e-12,  3.81064593e-01,  1.21613224e-01],\n",
-       "                     [-5.96149371e-16, -9.12099183e-01,  2.85798444e+00]])),\n",
-       "             ('kinds', ['domain', 'domain', 'domain']),\n",
-       "             ('endian', 'little'),\n",
-       "             ('encoding', 'raw'),\n",
-       "             ('space origin',\n",
-       "              array([-66.02582741,  -2.30053151, -81.77319682]))])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "adc_spc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ca025402",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# image: HxW (grayscale) or HxWx3 (RGB)\n",
-    "# mask:  HxW (binary or label mask)\n",
-    "\n",
-    "plt.figure(figsize=(6, 6))\n",
-    "\n",
-    "plt.imshow(adc[:,:,15], cmap=\"gray\")\n",
-    "plt.imshow(seg[:,:,15], cmap=\"Reds\", alpha=0.4)  # overlay mask\n",
-    "plt.axis(\"off\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8b5d382e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "args.num_classes = 4\n",
-    "args.mil_mode = \"att_trans\"\n",
-    "args.use_heatmap = True\n",
-    "args.tile_size = 64\n",
-    "args.tile_count = 24\n",
-    "args.depth = 3\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4cf061ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
-      "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"
-     ]
-    }
-   ],
-   "source": [
-    "args.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "\n",
-    "pirads_model = MILModel_3D(\n",
-    "    num_classes=args.num_classes,  \n",
-    "    mil_mode=args.mil_mode \n",
-    ")\n",
-    "pirads_checkpoint = torch.load(os.path.join(args.project_dir, 'models', 'pirads.pt'), map_location=\"cpu\")\n",
-    "pirads_model.load_state_dict(pirads_checkpoint[\"state_dict\"])\n",
-    "pirads_model.to(args.device)\n",
-    "\n",
-    "cspca_model = csPCa_Model(backbone=pirads_model).to(args.device)\n",
-    "checkpt = torch.load(os.path.join(args.project_dir, 'models', 'cspca_model.pth'), map_location=\"cpu\")\n",
-    "cspca_model.load_state_dict(checkpt['state_dict'])\n",
-    "cspca_model = cspca_model.to(args.device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fac15515",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform = data_transform(args)\n",
-    "files = os.listdir(args.t2_dir)\n",
-    "data_list = []\n",
-    "for file in files:\n",
-    "    temp = {}\n",
-    "    temp['image'] = os.path.join(args.t2_dir, file)\n",
-    "    temp['dwi'] = os.path.join(args.dwi_dir, file)\n",
-    "    temp['adc'] = os.path.join(args.adc_dir, file)\n",
-    "    temp['heatmap'] = os.path.join(args.heatmapdir, file)\n",
-    "    temp['mask'] = os.path.join(args.seg_dir, file)\n",
-    "    temp['label'] = 0  # dummy label\n",
-    "    data_list.append(temp)\n",
-    "\n",
-    "dataset = Dataset(data=data_list, transform=transform)\n",
-    "loader = torch.utils.data.DataLoader(\n",
-    "    dataset,\n",
-    "    batch_size=1,\n",
-    "    shuffle=False,\n",
-    "    num_workers=0,\n",
-    "    pin_memory=True,\n",
-    "    multiprocessing_context= None,\n",
-    "    sampler=None,\n",
-    "    collate_fn=list_data_collate,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "eb80047b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pirads_list = []\n",
-    "pirads_model.eval()\n",
-    "cspca_risk_list = []\n",
-    "cspca_model.eval()\n",
-    "top5_patches = []\n",
-    "with torch.no_grad():\n",
-    "    for idx, batch_data in enumerate(loader):\n",
-    "        data = batch_data[\"image\"].as_subclass(torch.Tensor).to(args.device)\n",
-    "        logits = pirads_model(data)\n",
-    "        pirads_score= torch.argmax(logits, dim=1)\n",
-    "        pirads_list.append(pirads_score.item())\n",
-    "\n",
-    "        output = cspca_model(data)\n",
-    "        output = output.squeeze(1)\n",
-    "        cspca_risk_list.append(output.item())\n",
-    "\n",
-    "        sh = data.shape\n",
-    "        x = data.reshape(sh[0] * sh[1], sh[2], sh[3], sh[4], sh[5])\n",
-    "        x = cspca_model.backbone.net(x)\n",
-    "        x = x.reshape(sh[0], sh[1], -1)\n",
-    "        x = x.permute(1, 0, 2)\n",
-    "        x = cspca_model.backbone.transformer(x)\n",
-    "        x = x.permute(1, 0, 2)\n",
-    "        a = cspca_model.backbone.attention(x)\n",
-    "        a = torch.softmax(a, dim=1)\n",
-    "        a = a.view(-1)\n",
-    "        top5_values, top5_indices = torch.topk(a, 5)\n",
-    "\n",
-    "        patches_top_5 = []\n",
-    "        for i in range(5):\n",
-    "            patch_temp = data[0, top5_indices.cpu().numpy()[i]][0].cpu().numpy()\n",
-    "            patches_top_5.append(patch_temp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "dbcfc97f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(patches_top_5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "4edb20e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import argparse\n",
-    "import os\n",
-    "import shutil\n",
-    "import time\n",
-    "import yaml\n",
-    "import sys\n",
-    "import gdown\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.distributed as dist\n",
-    "import torch.multiprocessing as mp\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "from monai.config import KeysCollection\n",
-    "from monai.metrics import Cumulative, CumulativeAverage\n",
-    "from monai.networks.nets import milmodel, resnet, MILModel\n",
-    "from monai.transforms import (\n",
-    "    Compose,\n",
-    "    GridPatchd,\n",
-    "    LoadImaged,\n",
-    "    MapTransform,\n",
-    "    RandFlipd,\n",
-    "    RandGridPatchd,\n",
-    "    RandRotate90d,\n",
-    "    ScaleIntensityRanged,\n",
-    "    SplitDimd,\n",
-    "    ToTensord,\n",
-    "    ConcatItemsd, \n",
-    "    SelectItemsd,\n",
-    "    EnsureChannelFirstd,\n",
-    "    RepeatChanneld,\n",
-    "    DeleteItemsd,\n",
-    "    EnsureTyped,\n",
-    "    ClipIntensityPercentilesd,\n",
-    "    MaskIntensityd,\n",
-    "    HistogramNormalized,\n",
-    "    RandBiasFieldd,\n",
-    "    RandCropByPosNegLabeld,\n",
-    "    NormalizeIntensityd,\n",
-    "    SqueezeDimd,\n",
-    "    CropForegroundd,\n",
-    "    ScaleIntensityd,\n",
-    "    SpatialPadd,\n",
-    "    CenterSpatialCropd,\n",
-    "    ScaleIntensityd,\n",
-    "    Transposed,\n",
-    "    RandWeightedCropd,\n",
-    ")\n",
-    "from sklearn.metrics import cohen_kappa_score\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from torch.utils.data.dataloader import default_collate\n",
-    "from torchvision.models.resnet import ResNet50_Weights\n",
-    "from src.data.custom_transforms import ClipMaskIntensityPercentilesd, NormalizeIntensity_customd\n",
-    "from torch.utils.data.distributed import DistributedSampler\n",
-    "from torch.utils.tensorboard import SummaryWriter\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import wandb\n",
-    "import math\n",
-    "from monai.data import Dataset, load_decathlon_datalist, ITKReader, NumpyReader, PersistentDataset\n",
-    "\n",
-    "from src.model.MIL import MILModel_3D\n",
-    "from src.model.csPCa_model import csPCa_Model\n",
-    "\n",
-    "import logging\n",
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e42cc132",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform_image = Compose(\n",
-    "    [\n",
-    "        LoadImaged(keys=[\"image\", \"mask\"], reader=ITKReader(), ensure_channel_first=True, dtype=np.float32),\n",
-    "        ClipMaskIntensityPercentilesd(keys=[\"image\"], lower=0, upper=99.5, mask_key=\"mask\"),\n",
-    "        NormalizeIntensity_customd(keys=[\"image\"], mask_key=\"mask\", channel_wise=True),\n",
-    "        EnsureTyped(keys=[\"label\"], dtype=torch.float32),\n",
-    "        ToTensord(keys=[\"image\", \"label\"]),\n",
-    "    ]\n",
-    ")\n",
-    "dataset_image = Dataset(data=data_list, transform=transform_image)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "bcdddd9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(270, 270, 28)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_image[0]['image'][0].numpy().shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "56072a2b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'image': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/t2_histmatched/1009449_11049598.nrrd',\n",
-       "  'dwi': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/DWI_histmatched/1009449_11049598.nrrd',\n",
-       "  'adc': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/ADC_histmatched/1009449_11049598.nrrd',\n",
-       "  'heatmap': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/heatmaps/1009449_11049598.nrrd',\n",
-       "  'mask': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/prostate_mask/1009449_11049598.nrrd',\n",
-       "  'label': 0}]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db1163d2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "foundation",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.21"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}