Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

Introduction

Pytorch implementation for MICCAI 2022 paper Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

Abstract

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classi- fication, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our ap- proach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynam- ically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis stag- ing and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks.

Setup

Package                Version
---------------------- -------------------
h5py                   3.1.0
numpy                  1.15.4
opencv-python          4.5.2.52
pandas                 1.1.5
SimpleITK              2.0.2
Scikit-learn           0.24.2
torch                  1.4.0
torchvision            0.5.0

Dara preparing

1. Download the iCTCF dataset: https://ngdc.cncb.ac.cn/ictcf/HUST-19.php

2. Segment the region of lung (a recommended unsupervised algorithm: COVID-MTL[1]).

[1] Bao, G., Chen, H., Liu, T., Gong, G., Yin, Y., Wang, L., Wang, X.: Covid-mtl: Multitask learning with shift3d and random-weighted loss for covid-19 diagnosis and severity assessment. Pattern Recognition 124, 108499 (2022) 6

3. Generate preprocessed data and the labels.

python preprocessing.py --img_path <path of raw data> --gt_path <path of your generated mask> --label_path <path of patients label>

4. Sort out the data and code. Put all the preprocessed data (.npy.h5) like this:

.
├── code
│   ├──datasets
│   │       └── dataset_*.py
│   ├──train.py
│   ├──test.py
│   └──...
├── models_save
│   └── iCTCF
└── data
    └──Synapse
        ├── iCTCF_train
        │   ├── Patient-1.npy.h5
        │   └── *.npy.h5
        └── iCTCF_test
            ├── Patient-1000.npy.h5
            └── *.npy.h5

Training

python train.py --max_epoch 45 --model_path <your model path>

Testing

python test.py --model_path <your model path>

Citation

If this repository is useful for your research, please cite:

   @article{ma2022ttadc,
     title={Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift},
     author={Ma, Wenao and Chen, Cheng and Zheng, Shuang and Qin, Jing and Zhang, Huimao and Dou, Qi},
     journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
     year={2022}
   }  

Contact

For any questions, please contact '[email protected]'