adds gradcam tutorial

README.md

@@ -170,3 +170,6 @@ This notebook demonstrates the image transformations on histology images using
 
 #### [varautoencoder_mednist](./modules/varautoencoder_mednist)
 This tutorial uses the MedNIST scan (or alternatively the MNIST) dataset to demonstrate MONAI's variational autoencoder class.
+
+#### [interpretability](./modules/interpretability)
+Tutorials in this folder demonstrate model visualisation and interpretability features of MONAI. Currently, it consists of class activation mapping and occlusion sensitivity for 3D classification model visualisations and analysis.

modules/interpretability/bbox_gen.py

@@ -0,0 +1,99 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+Script used to generate lesion/non-lesion patches from MSD lung.
+(requires numpy/torch/skimage.measure/monai)
+
+The original dataset could be downloaded via the MONAI API:
+
+    import monai
+    monai.apps.DecathlonDataset(root_dir="./", task="Task06_Lung", section="training", download=True)
+
+- After running these command, the dataset will be downloaded and unzipped at `root_dir`.
+- Lesion bounding boxes are generated from the connected components in the segmentation masks.
+- Non-lesion patches are generated from randomly from the volume.
+
+"""
+
+import glob
+import os
+import sys
+
+import numpy as np
+import skimage.measure as measure
+from monai.data import write_nifti
+from monai.transforms import (
+    AddChanneld,
+    BoundingRect,
+    LoadImaged,
+    RandWeightedCropd,
+    Resized,
+    SpatialCropd,
+)
+from monai.utils import set_determinism
+
+# optionally give folder
+folder = sys.argv[1] if len(sys.argv) > 1 else '.'
+# create output folder
+os.makedirs(folder+'/patch', exist_ok=True)
+set_determinism(0)
+
+image_names = sorted(glob.glob(folder+"/Task06_Lung/imagesTr/*"))
+label_names = sorted(glob.glob(folder+"/Task06_Lung/labelsTr/*"))
+if len(image_names) * len(label_names) == 0:
+    raise AssertionError('no images and/or labels found')
+
+data_names = [{"label": ll, "image": ii} for ll, ii in zip(label_names, image_names)]
+
+patch_size = (72, 72, 48)
+
+for name in data_names:
+    print(f"---on {name['label']}---")
+    name_id = os.path.basename(name["label"])
+    keys = ("image", "label")
+    data = LoadImaged(keys)(name)
+    labels, n_comp = measure.label(data["label"] == 1, connectivity=3, return_num=True)
+    print("total components", n_comp)
+    for i in range(n_comp + 1):
+        if i == 0:
+            continue  # skipping background
+        b_label = labels == i
+        bb = BoundingRect()(b_label[None])
+        area = (bb[0, 1] - bb[0, 0]) * (bb[0, 3] - bb[0, 2]) * (bb[0, 5] - bb[0, 4])
+        if area <= 500:
+            continue
+        print(bb, area)
+        s = [bb[0, 0] - 16, bb[0, 2] - 16, bb[0, 4] - 16]
+        e = [bb[0, 1] + 16, bb[0, 3] + 16, bb[0, 5] + 16]
+
+        # generate lesion patches based on the bounding boxes
+        data_out = AddChanneld(keys)(data)
+        data_out = SpatialCropd(keys, roi_start=s, roi_end=e)(data_out)
+        resize = Resized(keys, patch_size, mode=("trilinear", "nearest"))
+        data_out = resize(data_out)
+
+        patch_out = f"{folder}/patch/lesion_{s[0]}_{s[1]}_{s[2]}_{e[0]}_{e[1]}_{e[2]}_{name_id}"
+        label_out = f"{folder}/patch/labels_{s[0]}_{s[1]}_{s[2]}_{e[0]}_{e[1]}_{e[2]}_{name_id}"
+        write_nifti(data_out["image"][0], file_name=patch_out)
+        write_nifti(data_out["label"][0], file_name=label_out)
+
+        # generate random negative samples
+        rand_data_out = AddChanneld(keys)(data)
+        rand_data_out["inv_label"] = (
+            rand_data_out["label"] == 0
+        )  # non-lesion sampling map
+        rand_crop = RandWeightedCropd(keys, "inv_label", patch_size, num_samples=3)
+        rand_data_out = rand_crop(rand_data_out)
+        for idx, d in enumerate(rand_data_out):
+            if np.sum(d["label"]) > 0:
+                continue
+            write_nifti(d["image"][0], file_name=f"{folder}/patch/norm_{idx}_{name_id}")