https://github.com/tencent-ailab/rlogist

RLogist = RL (reinforcement learning) + Pathologist
https://github.com/tencent-ailab/rlogist

Last synced: 2 days ago
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RLogist = RL (reinforcement learning) + Pathologist

• Host: GitHub
• URL: https://github.com/tencent-ailab/rlogist
• Owner: tencent-ailab
• Created: 2022-11-30T12:03:37.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-14T14:58:50.000Z (almost 2 years ago)
• Last Synced: 2023-03-04T23:48:18.879Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 73.2 KB
• Stars: 47
• Watchers: 5
• Forks: 6
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# RLogist

RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning 

(paper accepted by AAAI 2023)

## Fully Automated Run

### Data Preparation

The data used for training and testing are expected to be organized as follows: 

- Digitized WSI data in well known standard formats (.svs, .ndpi, .tiff etc.) are stored under a folder named DATA_DIRECTORY 

```

DATA_DIRECTORY/

	├── slide_1.tif

	├── slide_2.tif

	└── ...

```

- WSI labels are recorded in a .CSV file: LABEL_LIST.csv

  | WSI_path                        | label |

  | ------------------------------- | ----- |

  | CAMELYON16/train/tumor_001.tif  | 1     |

  | CAMELYON16/train/normal_001.tif | 0     |

### Automated Run

```shell

python main.py --source DATA_DIRECTORY --label_list LABEL_LIST.csv

```

The script automatically reads the dataset and the corresponding labels for training. 

The segmentation and patching settings can be configured in `create_patches.py`, training parameters for RLogist can be configured in `main.py` (RL algorithm-specific hyper-parameters in source file like `ppo.py`)

### Evaluation

```shell

python eval_model.py --input_dir DATA_DIRECTORY --config_file CONFIG.yaml

```

The script automatically loads the model and reads the dataset and the corresponding labels for evaluation. 

## Components 

Follow the Guidance in corresponding directories:

### RL_env Test

```shell

python WSI_observation_env.py

```

### CLAM classifier Pretrain

```shell

python train_CLAM_model.py

```