MFC-MIL

The official implementation of "A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis. (ICLR 2025)"

Abstract

Multiple Instance Learning (MIL) in digital pathology Whole Slide Image (WSI) analysis has shown significant progress. However, due to data bias and unobservable confounders, this paradigm still faces challenges in terms of performance and interpretability. Existing MIL methods might identify patches that do not have true diagnostic significance, leading to false correlations, and experience difficulties in integrating multi-scale features and handling unobservable confounders. To address these issues, we propose a new Multi-Scale Frequency Domain Causal framework (MFC). This framework employs an adaptive memory module to estimate the overall data distribution through multi-scale frequency-domain information during training and simulates causal interventions based on this distribution to mitigate confounders in pathological diagnosis tasks. The framework integrates the Multi-scale Spatial Representation Module (MSRM), Frequency Domain Structure Representation Module (FSRM), and Causal Memory Intervention Module (CMIM) to enhance the model's performance and interpretability. Furthermore, the plug-and-play nature of this framework allows it to be broadly applied across various models. Experimental results on Camelyon16 and TCGA-NSCLC dataset show that, compared to previous work, our method has significantly improved accuracy and generalization ability, providing a new theoretical perspective for medical image analysis and potentially advancing the field further.

Usage

1. Installation

# clone the repository
git clone https://github.com/WissingChen/MFC-MIL.git

# create a conda environment
conda env create -f requirements.yml

2. Dataset

Preprocess TCGA Dataset

We use the same configuration of data preprocessing as DSMIL.

Preprocess CAMELYON16 Dataset

We use CLAM to preprocess CAMELYON16 at 20x. For your own dataset, you can modify and run create_patches_fp_Lung.py and extract_features_fp_LungRes18Imag.py.

The data used for training, validation and testing are expected to be organized as follows:

DATA_ROOT_DIR/
    ├──DATASET_1_DATA_DIR/
        └── pt_files
                ├── slide_1.pt
                ├── slide_2.pt
                └── ...
        └── h5_files
                ├── slide_1.h5
                ├── slide_2.h5
                └── ...
    ├──DATASET_2_DATA_DIR/
        └── pt_files
                ├── slide_a.pt
                ├── slide_b.pt
                └── ...
        └── h5_files
                ├── slide_i.h5
                ├── slide_ii.h5
                └── ...
    └── ...

3. Train

First, after preparing the data, you should modify the relevant parameters in the config folder, including the data path, etc.

Then, you can simply run the main.py file directly.

Citation

@inproceedings{cuimultiscale,
  title={A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis},
  author={Cui, Xiaoyu and Chen, Weixing and Su, Jiandong},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025},
}