README.md

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise

Yeonguk Yu · Minhwan Ko · Sungho Shin · Kangmin Kim · Kyoobin Lee
Artificial Intelligence LAB GIST, South Korea

NeurIPS 2024 - Poster Presentation

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TL;DR: We propose CUFIT, a robust fine-tuning method for vision foundation models under noisy label conditions, based on the advantages of linear probing and adapters.

Our CUrriculum FIne-Tuning of Vision Foundation Model (CUFIT) offers a robust training framework for medical multi-class image classification under noisy label conditions. Leveraging vision foundation models (VFMs) pretrained on large-scale datasets, CUFIT effectively handles noisy labels without modifying the feature extractor, using linear probing. Subsequently, it employs a curriculum fine-tuning approach, beginning with linear probing to ensure robustness to noisy samples, followed by fine-tuning two adapters for enhanced classification performance. CUFIT outperforms conventional methods across various medical image benchmarks, achieving superior results at various noise rates on datasets such as HAM10000 and APTOS-2019, highlighting its capability to address the challenges posed by noisy labels in medical datasets.

🚀 Getting Started

Clone the Repository

git clone https://github.com/gist-ailab/CUFIT.git
cd CUFIT

Environment Setup

This code is tested under Linux 20.04 and Python 3.8.18 environment, and the code requires following main packages to be installed:

• Pytorch: Tested under 2.0.1 version of Pytorch-GPU.
• torchvision: which will be installed along Pytorch. Tested under 0.15.2 version.
• MedMNIST: which is needed for experiments with BloodMnist, OrgancMnist. Tested under 3.0.1 version.

you may use the follwoing lines.

conda create -n cufit python=3.8
conda activate cufit
pip install -r requirement.txt

Dataset Preparation

Some public datasets are required to be downloaded for running experiments.

HAM10000 preparation

1. Download the training data, training ground truth, Test data, Test ground truth of task 3 in this link.

2. Place the zip files in "CUFIT/data" folder and extract them.

3. Run the python code "ham10000.py" in "CUFIT/data".

4. This will create a folder named "ham10000" where images are sorted by its corrseponding disease.

APTOS-2019 preparation

1. Download the zip files by clicking "download all" button in kaggle site.

2. Place the zip files in "CUFIT/data" folder and extract it.

3. Create a folder named "APTOS-2019" in "CUFIT/data".

4. Place the extracted files in the "APTOS-2019" folder.

Config file may need to be changed for your path to download. For example,

# conf/ham10000.json
{
    "epoch" : "100",
    "id_dataset" : "./data/ham10000",   # Your path to dataset
    "batch_size" : 32,
    "save_path" : "./checkpoints/ham10000",   # Your path to checkpoint
    "num_classes" : 7
}

Place the data and create checkpoint folder following this directory structure:

CUFIT/
├── assets/
├── checkpoints/
   ├── HAM10000/
   └── APTOS-2019/
├── conf/
   ├── HAM10000.json
   └── aptos.json
├── data/
   ├── HAM10000/
       ├── test/
       └── train/
   └── APTOS-2019
       ├── test_images/
       ├── train_images/
       ├── val_images/
       ├── test.csv
       ├── train_1.csv
       └── valid.csv
├── rein/
└── utils/

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How to Run

- To train a model by the linear probing with DINOv2-small architecture

python train_linear.py -d 'data_name' -g 'gpu-num' -n 'noise_rate' -s 'save_name'

for example,

python train_linear.py -d ham10000 -g 0 -n 0.2 -s dinov2s_linear_0.2

- To train a model by a single rein adapter with DINOv2-small architecture

python train_rein.py -d 'data_name' -g 'gpu-num' -n 'noise_rate -s 'save_name'

for example,

python train_rein.py -d ham10000 -g 0 -n 0.2 -s dinov2s_single_rein_0.2

- To train a model by the CUFIT with DINOv2-small architecture

python train_cuft.py -d 'data_name' -g 'gpu-num' -n 'noise_rate -s 'save_name'

for example,

python train_cufit.py -d ham10000 -g 0 -n 0.2 -s dinov2s_cufit_0.2

🤝 Acknowledgements & Support

This work waspartly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. RS-2022-II0951, Development of Uncertainty-Aware Agents Learning by Asking Questions, 90%) and Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No. 22-CM-GU-08, 10%.

🌟 License

The source code of this repository is released only for academic use. See the license file for details.

📚 Citation

If you use CUFIT in your research, please consider citing us.

@inproceedings{
yu2024curriculum,
title={Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise},
author={Yeonguk Yu and Minhwan Ko and Sungho Shin and Kangmin Kim and Kyoobin Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=vYUx8j5KK2}
}