Update tumor detection pipline with new components

pathology/tumor_detection/README.MD

@@ -12,12 +12,11 @@ The model is based on ResNet18 with the last fully connected layer replaced by a
 
 All the data used to train and validate this model is from [Camelyon-16 Challenge](https://camelyon16.grand-challenge.org/). You can download all the images for "CAMELYON16" data set from various sources listed [here](https://camelyon17.grand-challenge.org/Data/).
 
-Location information for training/validation patches (the location on the whole slide image where patches are extracted) are adopted from [NCRF/coords](https://github.com/baidu-research/NCRF/tree/master/coords). The reformatted coordinations and labels are stored in a json file (`dataset_0.json`), and can be downloaded from [here](https://drive.google.com/file/d/1m2pwko6hxwsxeDWZY2oSOV-_KT97Ol0o/view?usp=sharing)
+Location information for training/validation patches (the location on the whole slide image where patches are extracted) are adopted from [NCRF/coords](https://github.com/baidu-research/NCRF/tree/master/coords). The reformatted coordinations and labels in CSV format for training (`training.csv`) can be found [here](https://drive.google.com/file/d/1httIjgji6U6rMIb0P8pE0F-hXFAuvQEf/view?usp=sharing) and for validation (`validation.csv`) can be found [here](https://drive.google.com/file/d/1tJulzl9m5LUm16IeFbOCoFnaSWoB6i5L/view?usp=sharing).
 
-This pipeline expects the training/validation data (whole slide images) reside in `cfg["data_root"]/training/images`. By default `data_root` is pointing to `/workspace/data/medical/pathology/` You can easily modify it to point to a different directory by passing the following argument in the runtime: `--data-root /other/data/root/dir/`.
+This pipeline expects the training/validation data (whole slide images) reside in `cfg["data_root"]/training/images`. By default `data_root` is pointing to the code folder `./`; however, you can easily modify it to point to a different directory by passing the following argument in the runtime: `--data-root /other/data/root/dir/`.
 
-> `dataset_0_subset_0.json` is also provided [here](https://drive.google.com/file/d/1NCd0y4FR42maQpfZjzKlFSIX4oeKgysg/view?usp=sharing) to check the functionality of the pipeline using only two of the whole slide images: `tumor_001` and `tumor_101`. <br/>
-> This dataset should not be used for the real training or any perfomance evaluation.
+> [`training_sub.csv`](https://drive.google.com/file/d/1rO8ZY-TrU9nrOsx-Udn1q5PmUYrLG3Mv/view?usp=sharing) and [`validation_sub.csv`](https://drive.google.com/file/d/130pqsrc2e9wiHIImL8w4fT_5NktEGel7/view?usp=sharing) is also provided to check the functionality of the pipeline using only two of the whole slide images: `tumor_001` (for training) and `tumor_101` (for validation). This dataset should not be used for the real training or any performance evaluation.
 
 ### Input and output formats
 

pathology/tumor_detection/ignite/camelyon_train_evaluate.py

@@ -1,34 +1,17 @@
-import os
-
 import logging
+import os
 import time
 from argparse import ArgumentParser
 
 import numpy as np
-
+import pandas as pd
 import torch
-from torch.optim import SGD, lr_scheduler
-
 from ignite.metrics import Accuracy
+from torch.optim import SGD, lr_scheduler
 
 import monai
-from monai.data import DataLoader, load_decathlon_datalist
-from monai.transforms import (
-    ActivationsD,
-    AsDiscreteD,
-    CastToTypeD,
-    Compose,
-    RandFlipD,
-    RandRotate90D,
-    RandZoomD,
-    ScaleIntensityRangeD,
-    ToNumpyD,
-    TorchVisionD,
-    ToTensorD,
-)
-from monai.utils import first, set_determinism
-from monai.optimizers import Novograd
-from monai.engines import SupervisedTrainer, SupervisedEvaluator
+from monai.data import DataLoader, PatchWSIDataset, CSVDataset
+from monai.engines import SupervisedEvaluator, SupervisedTrainer
 from monai.handlers import (
     CheckpointSaver,
     LrScheduleHandler,
@@ -37,10 +20,24 @@
     ValidationHandler,
     from_engine,
 )
-
-from monai.apps.pathology.data import PatchWSIDataset
 from monai.networks.nets import TorchVisionFCModel
-
+from monai.optimizers import Novograd
+from monai.transforms import (
+    Activationsd,
+    AsDiscreted,
+    CastToTyped,
+    Compose,
+    GridSplitd,
+    Lambdad,
+    RandFlipd,
+    RandRotate90d,
+    RandZoomd,
+    ScaleIntensityRanged,
+    ToNumpyd,
+    TorchVisiond,
+    ToTensord,
+)
+from monai.utils import first, set_determinism
 
 torch.backends.cudnn.enabled = True
 set_determinism(seed=0, additional_settings=None)
@@ -65,7 +62,7 @@ def set_device(cfg):
     if gpus and torch.cuda.is_available():
         os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(n) for n in gpus])
         device = torch.device("cuda")
-        print(f'CUDA is being used with GPU ID(s): {os.environ["CUDA_VISIBLE_DEVICES"]}')
+        print(f'CUDA is being used with GPU Id(s): {os.environ["CUDA_VISIBLE_DEVICES"]}')
     else:
         device = torch.device("cpu")
         print("CPU only!")
@@ -82,54 +79,66 @@ def train(cfg):
     # Build MONAI preprocessing
     train_preprocess = Compose(
         [
-            ToTensorD(keys="image"),
-            TorchVisionD(
+            Lambdad(keys="label", func=lambda x: x.reshape((1, cfg["grid_shape"], cfg["grid_shape"]))),
+            GridSplitd(
+                keys=("image", "label"),
+                grid=(cfg["grid_shape"], cfg["grid_shape"]),
+                size={"image": cfg["patch_size"], "label": 1},
+            ),
+            ToTensord(keys=("image")),
+            TorchVisiond(
                 keys="image", name="ColorJitter", brightness=64.0 / 255.0, contrast=0.75, saturation=0.25, hue=0.04
             ),
-            ToNumpyD(keys="image"),
-            RandFlipD(keys="image", prob=0.5),
-            RandRotate90D(keys="image", prob=0.5),
-            CastToTypeD(keys="image", dtype=np.float32),
-            RandZoomD(keys="image", prob=0.5, min_zoom=0.9, max_zoom=1.1),
-            ScaleIntensityRangeD(keys="image", a_min=0.0, a_max=255.0, b_min=-1.0, b_max=1.0),
-            ToTensorD(keys=("image", "label")),
+            ToNumpyd(keys="image"),
+            RandFlipd(keys="image", prob=0.5),
+            RandRotate90d(keys="image", prob=0.5, max_k=3, spatial_axes=(-2, -1)),
+            CastToTyped(keys="image", dtype=np.float32),
+            RandZoomd(keys="image", prob=0.5, min_zoom=0.9, max_zoom=1.1),
+            ScaleIntensityRanged(keys="image", a_min=0.0, a_max=255.0, b_min=-1.0, b_max=1.0),
+            ToTensord(keys=("image", "label")),
         ]
     )
     valid_preprocess = Compose(
         [
-            CastToTypeD(keys="image", dtype=np.float32),
-            ScaleIntensityRangeD(keys="image", a_min=0.0, a_max=255.0, b_min=-1.0, b_max=1.0),
-            ToTensorD(keys=("image", "label")),
+            Lambdad(keys="label", func=lambda x: x.reshape((1, cfg["grid_shape"], cfg["grid_shape"]))),
+            GridSplitd(
+                keys=("image", "label"),
+                grid=(cfg["grid_shape"], cfg["grid_shape"]),
+                size={"image": cfg["patch_size"], "label": 1},
+            ),
+            CastToTyped(keys="image", dtype=np.float32),
+            ScaleIntensityRanged(keys="image", a_min=0.0, a_max=255.0, b_min=-1.0, b_max=1.0),
+            ToTensord(keys=("image", "label")),
         ]
     )
     # __________________________________________________________________________
     # Create MONAI dataset
-    train_json_info_list = load_decathlon_datalist(
-        data_list_file_path=cfg["dataset_json"],
-        data_list_key="training",
-        base_dir=cfg["data_root"],
+    train_data_list = CSVDataset(
+        cfg["train_file"],
+        col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
+        kwargs_read_csv={"header": None},
+        transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
     )
-    valid_json_info_list = load_decathlon_datalist(
-        data_list_file_path=cfg["dataset_json"],
-        data_list_key="validation",
-        base_dir=cfg["data_root"],
+    train_dataset = PatchWSIDataset(
+        data=train_data_list,
+        patch_size=cfg["region_size"],
+        patch_level=0,
+        transform=train_preprocess,
+        reader="openslide" if cfg["use_openslide"] else "cuCIM",
     )
 
-    train_dataset = PatchWSIDataset(
-        train_json_info_list,
-        cfg["region_size"],
-        cfg["grid_shape"],
-        cfg["patch_size"],
-        train_preprocess,
-        image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
+    valid_data_list = CSVDataset(
+        cfg["valid_file"],
+        col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
+        kwargs_read_csv={"header": None},
+        transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
     )
     valid_dataset = PatchWSIDataset(
-        valid_json_info_list,
-        cfg["region_size"],
-        cfg["grid_shape"],
-        cfg["patch_size"],
-        valid_preprocess,
-        image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
+        data=valid_data_list,
+        patch_size=cfg["region_size"],
+        patch_level=0,
+        transform=valid_preprocess,
+        reader="openslide" if cfg["use_openslide"] else "cuCIM",
     )
 
     # __________________________________________________________________________
@@ -141,12 +150,10 @@ def train(cfg):
         valid_dataset, num_workers=cfg["num_workers"], batch_size=cfg["batch_size"], pin_memory=True
     )
 
-    # __________________________________________________________________________
-    # Get sample batch and some info
+    # Check first sample
     first_sample = first(train_dataloader)
     if first_sample is None:
-        raise ValueError("Fist sample is None!")
-
+        raise ValueError("First sample is None!")
     print("image: ")
     print("    shape", first_sample["image"].shape)
     print("    type: ", type(first_sample["image"]))
@@ -194,9 +201,7 @@ def train(cfg):
         StatsHandler(output_transform=lambda x: None),
         TensorBoardStatsHandler(log_dir=log_dir, output_transform=lambda x: None),
     ]
-    val_postprocessing = Compose(
-        [ActivationsD(keys="pred", sigmoid=True), AsDiscreteD(keys="pred", threshold=0.5)]
-    )
+    val_postprocessing = Compose([Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold=0.5)])
     evaluator = SupervisedEvaluator(
         device=device,
         val_data_loader=valid_dataloader,
@@ -219,9 +224,7 @@ def train(cfg):
             log_dir=cfg["logdir"], tag_name="train_loss", output_transform=from_engine(["loss"], first=True)
         ),
     ]
-    train_postprocessing = Compose(
-        [ActivationsD(keys="pred", sigmoid=True), AsDiscreteD(keys="pred", threshold=0.5)]
-    )
+    train_postprocessing = Compose([Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold=0.5)])
 
     trainer = SupervisedTrainer(
         device=device,
@@ -241,24 +244,18 @@ def train(cfg):
 def main():
     logging.basicConfig(level=logging.INFO)
     parser = ArgumentParser(description="Tumor detection on whole slide pathology images.")
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="../dataset_0.json",
-        dest="dataset_json",
-        help="path to dataset json file",
-    )
     parser.add_argument(
         "--root",
         type=str,
-        default="/workspace/data/medical/pathology/",
-        dest="data_root",
-        help="path to root folder of images containing training folder",
+        default="/workspace/data/medical/pathology",
+        help="path to image folder containing training/validation",
     )
+    parser.add_argument("--train-file", type=str, default="training.csv", help="path to training data file")
+    parser.add_argument("--valid-file", type=str, default="validation.csv", help="path to training data file")
     parser.add_argument("--logdir", type=str, default="./logs/", dest="logdir", help="log directory")
 
     parser.add_argument("--rs", type=int, default=256 * 3, dest="region_size", help="region size")
-    parser.add_argument("--gs", type=int, default=3, dest="grid_shape", help="image grid shape (3x3)")
+    parser.add_argument("--gs", type=int, default=3, dest="grid_shape", help="image grid shape e.g 3 means 3x3")
     parser.add_argument("--ps", type=int, default=224, dest="patch_size", help="patch size")
     parser.add_argument("--bs", type=int, default=64, dest="batch_size", help="batch size")
     parser.add_argument("--ep", type=int, default=10, dest="n_epochs", help="number of epochs")