RCC_DS
Code for paper "Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images". The dataset will be released soon. The original dataset comes from TCGA (KIRC, KIRP, KICH) project. https://portal.gdc.cancer.g
Summary
| Latest Version | Unknown |
|---|---|
| License | Unknown |
| CI Status | Failing |
| Stars | 2 |
| Forks | 1 |
| Open Issues | 0 |
| Last Commit | 2024-05-31 |
| Downloads | 0 |
| Last Indexed | 2026-08-11 05:07 |
Installation
nimble install RCC_DS
choosenim install RCC_DS
git clone https://gitlab.com/BioAI/RCC_DS
OS Compatibility
| Platform | Linux | macOS | Windows | FreeBSD | OpenBSD | NetBSD | Android | iOS | WASM | Embedded |
|---|---|---|---|---|---|---|---|---|---|---|
| RCC_DS | ✓ | ✓ | ✓ | - | - | - | - | - | - | - |
Source
| Repository | https://gitlab.com/BioAI/RCC_DS |
|---|---|
| Homepage | https://gitlab.com/BioAI/RCC_DS |
| Registry Source | gitlab |
README
RCC Detection and Subtyping Framework
Code for paper "Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images".
The original dataset comes from TCGA (KIRC, KIRP, KICH) project.
The annotated dataset is released on https://dataset.chenli.group/home/rcc-region-and-subtyping.
Setups
The requirement is as bellow:
- Python 3+
- PyTorch 1.3.1
- Torchvision 0.4.2
- numpy 1.16.2
- PIL 6.2.1
- matplotlib 3.0.2
- tqdm 4.28.1
- sklearn 0.20.1
- Openslide 1.1.1
- cv2 4.1.2.30
- pandas 0.23.4
Running
Detection (CCRCC example)
# train
python RCC_detection_train.py --epochs 200 --labeled_data_files your_labeled_images.txt --unlabeled_files your_unlabeled_images.txt --test_files valid_images.txt --out rcc@detection
# finetune
python RCC_detection_train.py --epochs 205 --resume rcc@detection/checkpoint.pth.tar --transfer True --labeled_data_files your_labeled_images.txt --unlabeled_files other_unlabeled_images.txt --test_files valid_images.txt --out rcc@detection@transfer
# predict
python RCC_predict.py --num_classes 2 --file_path_base rcc@detection@transfer/checkpoint.pth.tar --test_files your_unlabeled_images.txt --output_files your_unlabeled_predicted.txt
python RCC_predict.py --num_classes 2 --file_path_base rcc@detection@transfer/checkpoint.pth.tar --test_files other_unlabeled_images.txt --output_files other_unlabeled_predicted.txt
cat your_unlabeled_predicted.txt,other_unlabeled_predicted.txt -> ccrcc_predicted.txt
Subtyping
# train
cat ccrcc_predicted.txt,prcc_predicted.txt,chrcc_predicted.txt -> subtype_train_images.txt
python RCC_subtyping_train.py --num_classes 4 --model_path_base ./subtype_model/checkpoint.pth --model_path_best ./subtype_model/model_best.pth --train_files subtype_train_images.txt --test_files subtype_valid_images.txt
# predict
python RCC_predict.py --num_classes 4 --file_path_base ./subtype_model/model_best.pth --test_files subtype_test_images.txt