Detecting Invasive Ductal Carcinoma with Semi-Supervised Conditional GANs

TL;DR

This paper introduces a semi-supervised cGAN-based algorithm for automatic detection of invasive ductal carcinoma, aiming to improve diagnostic accuracy and assist pathologists in breast cancer analysis.

Contribution

The study presents a novel semi-supervised cGAN framework that outperforms baseline CNN models in IDC detection tasks.

Findings

Improved detection scores over baseline CNN models

Effective semi-supervised learning approach

Potential to assist clinical diagnosis

Abstract

Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers. The detection of IDC is a necessary preprocessing step in determining the aggressiveness of the cancer, determining treatment protocols, and predicting patient outcomes, and is usually performed manually by an expert pathologist. Here, we describe a novel algorithm for automatically detecting IDC using semi-supervised conditional generative adversarial networks (cGANs). The framework is simple and effective at improving scores on a range of metrics over a baseline CNN.