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DAX-Net

About

overview

Overview of DAX-Net. DAX-Net includes a feature extraction block, a feature fusion block, and a prediction block. The feature extraction block comprises two simultaneous CNN- and Transformer-based branches, generating two heterogeneous feature representations. During training, the feature fusion block fuses the two feature representations via summation and the prediction block uses them to separately conduct categorical classification and ordinal classification. DAX-Net is optimized using two loss functions that are tailored to the two classification tasks. At inference, the two feature representations are adaptively fused to focus more on the informative features.

Datasets

There are five datasets for two organs (colon & prostate) used in this study:

  1. Colon dataset:

  2. Prostate dataset:

datasets

Dataset stastitics.

For convenience, please prepare the downloaded datasets as shown in the directory tree below:

datasets
|__KBSMC_colon_tma_cancer_grading_1024 # CTrain, CValid, CTest-I
|__KBSMC_test_2 # CTest-II
|__prostate_harvard # PTrain, PValid, PTest-I
|__prostate_miccai_2019_patches_690_80_step05_test # PTest-II
|__AGGC22_patch_512_c08 # PTest-III

How to run

Requirements

Install necessary libraries in requirements.txt:

pip install -r requirements.txt

Training

For training DAX-Net, please run the below command

CUDA_VISIBLE_DEVICES=0 python3.8 train_val.py \
--dataset [DATASET NAME] \
--run_info=MULTI

DATASET NAME has two options: colon_tma and prostate_uhu. Set colon_tma for training, validating and saving checkpoints using on CTrain and CValid datasets. And set prostate_uhu for PTrain, PValid.

Reproducion

For reproduce our results, please run below command:

CUDA_VISIBLE_DEVICES=0 python3.8 test_only.py --dataset=[DATASET NAME] \
--checkpoint=[CHECKPOINT PATH] \
--run_info=MULTI

DATASET NAME has five options: colon_tma_test_1, colon_tma_test_2, prostate_uhu, prostate_ubc and aggc2022, for evaluating DAX-Net on CTest-I, CTest-II, PTest-I, PTest-II and PTest-III, respectively.

For [CHECKPOINT PATH], refers to the checkpoint links for corresponding organs: Colon and Prostate.

The results should be the same as those reported in Tables 2 and 3 in our paper.

Authors

Doanh C. Bui, Boram Song, Kyungeun Kim and Jin Tae Kwak

Citation

If any part of this code is used, please give appropriate citation to our paper.

BibTex entry: 

@article{BUI2024108112,
title = {DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images},
journal = {Computer Methods and Programs in Biomedicine},
volume = {248},
pages = {108112},
year = {2024},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2024.108112},
url = {https://www.sciencedirect.com/science/article/pii/S0169260724001081},
author = {Doanh C. Bui and Boram Song and Kyungeun Kim and Jin Tae Kwak},
}