# Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder

## TL;DR

This paper introduces a self-interpretable, unsupervised method for breast cancer histopathology image classification that uses a fully convolutional autoencoder to detect abnormal patterns with minimal annotations, aiding diagnosis and understanding.

## Contribution

The paper presents a novel unsupervised approach combining a fully convolutional autoencoder with contrast pattern mining for breast cancer diagnosis, requiring minimal annotation.

## Key findings

- Outperforms existing methods on public datasets
- Provides visual probability maps for interpretability
- Assists pathologists with verification and understanding

## Abstract

Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Our results, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.