Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
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This repository covers the official implementation of our Dual-Query multiple instance learning approach for histopathological image analysis, a BMVC 2023 (Oral) paper. A novel method for histopathological slide assessment, extending on the perceiver architecture and leveraging a dynamic meta-embedding strategy.
Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
Simon Holdenried-Krafft1, Peter Somers3, Ivonne A. Montes-Majarro2, Diana Silimon2, Cristina Tarín3, Falko Fend2, Hendrik P. A. Lensch1
1University of Tübingen, 2University Hospital of Tübingen, 3University of Stuttgart
Setup
A conda environment is used for dependency management
conda create -n dqmil python=3.7
conda activate dqmil
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
Running
The configuration is based on hydra. The settings can be found in the "configs" folder.
To train the Dual-Query Perceiver run:
python ./src/train.py
Datasets
The implementation relies on lmdbs. The corresponding code to create the datasets will be released in the upcoming weeks.
Citation
If you find this code useful, please consider citing:
@inproceedings{Holdenried-Krafft_2023_BMVC,
author = {Simon Holdenried-Krafft and Peter Somers and Ivonne Montes-Mojarro and Diana Silimon and Cristina Tarín and Falko Fend and Hendrik P. A. Lensch},
title = {Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0575.pdf}
}