update README under the pathology folder (Project-MONAI#1279)

Fixes # .

### Description
Update README under the pathology folder to be consistent with GitLab.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

Signed-off-by: KumoLiu <[email protected]>

pathology/multiple_instance_learning/README.md

@@ -2,7 +2,7 @@
 # Multiple Instance Learning (MIL) Examples
 
 This tutorial contains a baseline method of Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI).
-The dataset is from  [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate histology WSIs.
+The dataset is from the [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate histology WSIs.
 The implementation is based on:
 
 Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu: "Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging". In MICCAI (2021). [arXiv](https://arxiv.org/abs/2111.01556)
@@ -48,11 +48,11 @@ python ./panda_mil_train_evaluate_pytorch_gpu.py -h
 ### Train
 
 Train in multi-gpu mode with AMP using all available gpus,
-assuming the training images in `/PandaChallenge2020/train_images` folder,
+assuming the training images are in the `/PandaChallenge2020/train_images` folder,
 it will use the pre-defined 80/20 data split in [datalist_panda_0.json](https://drive.google.com/drive/u/0/folders/1CAHXDZqiIn5QUfg5A7XsK1BncRu6Ftbh)
 
 ```bash
-python -u panda_mil_train_evaluate_pytorch_gpu.py
+python -u panda_mil_train_evaluate_pytorch_gpu.py \
     --data_root=/PandaChallenge2020/train_images \
     --amp \
     --distributed \
@@ -65,7 +65,7 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py
 If you need to use only specific gpus, simply add the prefix `CUDA_VISIBLE_DEVICES=...`
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py \
     --data_root=/PandaChallenge2020/train_images \
     --amp \
     --distributed \
@@ -81,7 +81,7 @@ Run inference of the best checkpoint over the validation set
 
 ```bash
 # Validate checkpoint on a single gpu
-python -u panda_mil_train_evaluate_pytorch_gpu.py
+python -u panda_mil_train_evaluate_pytorch_gpu.py \
     --data_root=/PandaChallenge2020/train_images \
     --amp \
     --mil_mode=att_trans \
@@ -92,12 +92,12 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py
 ### Inference
 
 Run inference on a different dataset. It's the same script as for validation,
-we just specify a different data_root and json list files
+we just specify a different data_root and JSON list files
 
 ```bash
-python -u panda_mil_train_evaluate_pytorch_gpu.py
+python -u panda_mil_train_evaluate_pytorch_gpu.py \
     --data_root=/PandaChallenge2020/some_other_files \
-    --dataset_json=some_other_files.json
+    --dataset_json=some_other_files.json \
     --amp \
     --mil_mode=att_trans \
     --checkpoint=./logs/model.pt \

pathology/tumor_detection/README.MD

@@ -2,27 +2,27 @@
 
 ## Description
 
-Here we use a classification model to classify small batches extracted from very large whole-slide histopathology images. Since the patches are very small compare to the whole image, we can then use this model for detection of tumor in different area of a whole-slide pathology image.
+Here we use a classification model to classify small batches extracted from very large whole-slide histopathology images. Since the patches are very small compare to the whole image, we can then use this model for the detection of tumors in a different area of a whole-slide pathology image.
 
 ## Model Overview
 
 The model is based on ResNet18 with the last fully connected layer replaced by a 1x1 convolution layer.
 
 ## Data
 
-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/).
+All the data used to train and validate this model is from the [Camelyon-16 Challenge](https://camelyon16.grand-challenge.org/). You can download all the images for the "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 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).
+Location information for training/validation patches (the location on the whole slide image where patches are extracted) is 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 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/`.
 
 > [`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
 
-Input for the training pipeline is a json file (dataset.json) which includes path to each WSI, the location and the label information for each training patch.
+Input for the training pipeline is a JSON file (dataset.json) which includes the path to each WSI, the location and the label information for each training patch.
 
-Output of the network is the probability of whether the input patch contains tumor or not.
+The output of the network is the probability of whether the input patch contains the tumor or not.
 
 ## Disclaimer