Mix-and-Match Multiple Instance Learning (MM-MIL)

This is the code repository for our paper: Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue [https://doi.org/10.1364/BOE.480687].

Overview

We propose MM-MIL for the discovery of novel optical signatures when only coarse-grained and ambiguous annotations are available. We applied the proposed method to the investigation of human breast cancer-related optical signatures based on Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy and unveiled non-obvious cancer-related optical signatures in peri-tumoral regions.

System Environment

• Ubuntu 18.04
• Python
• Pytorch
• Nvidia GPU + CUDA

1. jsondiff
2. tdpm
3. tifffile
4. Captum

Usage

(Will be updated)

Main Results

(Will be updated)

Citation

If you use this code and relevant data, please cite our paper:

@article{shi2023weakly,
  title={Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue},
  author={Shi, Jindou and Tu, Haohua and Park, Jaena and Marjanovic, Marina and Higham, Anna M and Luckey, Natasha N and Cradock, Kimberly A and Liu, Z George and Boppart, Stephen A},
  journal={Biomedical Optics Express},
  volume={14},
  number={4},
  pages={1339--1354},
  year={2023},
  publisher={Optica Publishing Group}
}