984 fixes notebook (#986)

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Fixes #984


### Checks
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- [ ] Notebook runs automatically `./runner [-p <regex_pattern>]`

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

3d_classification/densenet_training_array.ipynb

@@ -462,10 +462,10 @@
     "# For the other dimensions (channel, width, height), use\n",
     "# -1 to use 0 and img.shape[x]-1 for min and max, respectively\n",
     "depth_slice = img.shape[2] // 2\n",
-    "occ_sens_b_box = [-1, -1, depth_slice, depth_slice, -1, -1, -1, -1]\n",
+    "occ_sens_b_box = [depth_slice-1, depth_slice, -1, -1, -1, -1]\n",
     "\n",
     "occ_result, _ = occ_sens(x=img, b_box=occ_sens_b_box)\n",
-    "occ_result = occ_result[..., label.argmax().item()]\n",
+    "occ_result = occ_result[0, label.argmax().item()][None]\n",
     "\n",
     "fig, axes = plt.subplots(1, 2, figsize=(25, 15), facecolor=\"white\")\n",
     "\n",
@@ -513,7 +513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,

modules/interpretability/class_lung_lesion.ipynb

@@ -11,7 +11,7 @@
     "The goal is to demonstrate MONAI's class activation mapping functions for visualising the classification models.\n",
     "\n",
     "For the demo data:\n",
-    "- Please see the `bbox_gen.py` script for generating the patch classification data from MSD task06_lung (available via `monai.apps.DecathlonDataset`).\n",
+    "- Please see the `bbox_gen.py` script for generating the patch classification data from MSD task06_lung (available via `monai.apps.DecathlonDataset`);\n",
     "- Alternatively, the patch dataset (~130MB) is available for direct downloading at: https://drive.google.com/drive/folders/1pQdzdkkC9c2GOblLgpGlG3vxsSK9NtDx\n",
     "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/interpretability/class_lung_lesion.ipynb)"
@@ -592,9 +592,9 @@
     "\n",
     "# For occlusion sensitivity, inference must be run many times. Hence, we can use a\n",
     "# bounding box to limit it to a 2D plane of interest (z=the_slice) where each of\n",
-    "# the arguments are the min and max for each of the dimensions (in this case CHWD).\n",
+    "# the arguments are the min and max for each of the spatial dimensions (in this case HWD).\n",
     "the_slice = train_ds[0][\"image\"].shape[-1] // 2\n",
-    "occ_sens_b_box = [-1, -1, -1, -1, -1, -1, the_slice, the_slice]"
+    "occ_sens_b_box = [-1, -1, -1, -1, the_slice-1, the_slice]"
    ]
   },
   {
@@ -666,7 +666,7 @@
     "\n",
     "    # run occlusion\n",
     "    occ_result, _ = occ_sens(x=image, b_box=occ_sens_b_box)\n",
-    "    occ_result = occ_result[..., pred_label]\n",
+    "    occ_result = occ_result[0, pred_label][None, None, ..., -1]\n",
     "\n",
     "    for row, (im, title) in enumerate(\n",
     "        zip(\n",
@@ -762,7 +762,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,