NeurIPS 2023 workshop - MedFM: Foundation Model Prompting for Medical Image Classification Challenge 2023

Congratulation to MedFCMA gained the 4th place !!!!

🛠️ Installation

Install requirements by

$ conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.1 -c pytorch
$ pip install mmcls==0.25.0 openmim scipy scikit-learn ftfy regex tqdm
$ mim install mmcv-full==1.6.0

Data preparation

Prepare data following MMClassification. The data structure looks like below:

data/
├── MedFMC
│   ├── chest
│   │   ├── images
│   │   ├── chest_X-shot_train_expY.txt
│   │   ├── chest_X-shot_val_expY.txt
│   │   ├── train_20.txt
│   │   ├── val_20.txt
│   │   ├── trainval.txt
│   │   ├── test_WithLabel.txt
│   ├── colon
│   │   ├── images
│   │   ├── colon_X-shot_train_expY.txt
│   │   ├── colon_X-shot_val_expY.txt
│   │   ├── train_20.txt
│   │   ├── val_20.txt
│   │   ├── trainval.txt
│   │   ├── test_WithLabel.txt
│   ├── endo
│   │   ├── images
│   │   ├── endo_X-shot_train_expY.txt
│   │   ├── endo_X-shot_val_expY.txt
│   │   ├── train_20.txt
│   │   ├── val_20.txt
│   │   ├── trainval.txt
│   │   ├── test_WithLabel.txt

Noted that the .txt files includes data split information for fully supervised learning and few-shot learning tasks. The public dataset is splited to trainval.txt and test_WithLabel.txt, and trainval.txt is also splited to train_20.txt and val_20.txt where 20 means the training data makes up 20% of trainval.txt. And the test_WithoutLabel.txt of each dataset is validation set.

Corresponding .txt files are stored at ./data_backup/ folder, the few-shot learning data split files {dataset}_{N_shot}-shot_train/val_exp{N_exp}.txt could also be generated as below:

python tools/generate_few-shot_file.py

Where N_shot is 1,5 and 10, respectively, the shot is of patient(i.e., 1-shot means images of certain one patient are all counted as one), not number of images.

The images in each dataset folder contains its images, which could be achieved from original dataset.

Training and evaluation using OpenMMLab codebases.

In this repository we provided many config files for fully supervised task (only uses 20% of original traning set, please check out the .txt files which split dataset) and few-shot learning task.

The config files of fully supervised transfer learning task are stored at ./configs/densenet, ./configs/efficientnet, ./configs/vit-base and ./configs/swin_transformer folders, respectively. The config files of few-shot learning task are stored at ./configs/ablation_exp and ./configs/vit-b16_vpt folders.

You can generate all prediction results of endo_N-shot_submission.csv, colon_N-shot_submission.csv and chest_N-shot_submission.csv and zip them into result.zip file. Then upload it to Grand Challenge website.

result/
├── endo_1-shot_submission.csv
├── endo_5-shot_submission.csv
├── endo_10-shot_submission.csv
├── colon_1-shot_submission.csv
├── colon_5-shot_submission.csv
├── colon_10-shot_submission.csv
├── chest_1-shot_submission.csv
├── chest_5-shot_submission.csv
├── chest_10-shot_submission.csv