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

A naive baseline and submission demo for the Foundation Model Prompting for Medical Image Classification Challenge 2023 (MedFM).

It is base on the MMPreTrain, it has backbone of ViT-cls, ViT-eva02, ViT-dinov2, Swin-cls and ViT-clip. More details could be found in its document.

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 openmim scipy scikit-learn ftfy regex tqdm
$ mim install mmpretrain

Results

The results of ChestDR, ColonPath and Endo in MedFMC dataset and their corresponding configs on each task are shown as below.

Few-shot Learning Results (10-shot/AUC)

We utilize Visual Prompt Tuning method as the few-shot learning baseline, The results are shown as below:

Dataset \ Backbone	Swin(384)	ViT-cls(384)	ViT-eva2(448)	ViT-dinov2(512)	ViT-clip(384)
ChestDR (10-shot)	64.66	67.96	65.69	67.16	66.60
ColonPath (10-shot)	97.62	98.13	97.62	97.30	98.11
Endo (10-shot)	64.89	66.18	67.78	64.40	65.79

All the bakcbones use 'base' arch.

Usage

Preparation

Prepare data following MMPreTrain. Download the dataset and unzip the '.zip' files into {$MedFM}/data/ as following:

MedFM (root)/
    ├── docker               # dockerfiles
    ├── configs              # all the configs
    │   ├── clip-b_vpt
    │   ├── swin-b_vpt
    │   ├── dinov2-b_vpt
    │   └── ...
    ├── data               
    │   ├── MedFMC_train      # unzip the download file
    │   ├── MedFMC_val
    │   └── ...
    ├── data_anns
    │   ├── MedFMC
    │   |    ├── chest
    │   |    ├── colon
    │   |    ├── endo
    │   ├── result             # sample example to submit 
    │   └── ...
    ├── medfmc                 # all source code
    ├── tools                  # train, test and other tools
    └── ...

click to show the detail of data_anns

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_anns/ 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 Test

For the training and testing, you can directly use commands below to train and test the model:

# you need to export path in terminal so the `custom_imports` in config would work
export PYTHONPATH=$PWD:$PYTHONPATH
# Training
python tools/train.py $CONFIG

# Evaluation
python tools/test.py $CONFIG $CHECKPOINT

Generating Submission results

Run

python tools/infer.py $CONFIF $WEIGHT $IMAGE_FOLDER --batch-size 4 --out $OUT_FILE_PATH

Using MedFMC repo with Docker

click to show the detail

More details of Docker could be found in this tutorial.

Preparation of Docker

We provide a Dockerfile to build an image. Ensure that your docker version >=19.03.

# build an image with PyTorch 1.11, CUDA 11.3
# If you prefer other versions, just modified the Dockerfile
docker build -t medfmc docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/medfmc/data medfmc

Build Docker and make sanity test

The submitted docker will be evaluated by the following command:

docker container run --gpus all --shm-size=8g -m 28G -it --name teamname --rm -v $PWD:/medfmc_exp -v $PWD/data:/medfmc_exp/data teamname:latest /bin/bash -c "sh /medfmc_exp/run.sh"

--gpus: specify the available GPU during inference
-m: spedify the maximum RAM
--name: container name during running
--rm: remove the container after running
-v $PWD:/medfmc_exp: map local codebase folder to Docker medfmc_exp folder.
-v $PWD/data:/medfmc_exp/data: map local codebase folder to Docker medfmc_exp/data folder.
teamname:latest: docker image name (should be teamname) and its version tag. The version tag should be latest. Please do not use v0, v1... as the version tag
/bin/bash -c "sh run.sh": start the prediction command.

Assuming the team name is baseline, the Docker build command is

docker build -t baseline .

During the inference, please monitor the GPU memory consumption using watch nvidia-smi. The GPU memory consumption should be less than 10G. Otherwise, it will run into an OOM error on the official evaluation server.

3) Save Docker

docker save baseline | gzip -c > baseline.tar.gz

License

This project is released under the Apache 2.0 license.

Citation

TO BE ADDED.

Adrian398/MedFM_Challenge_2023