TagGAN: A Generative Model for Data Tagging

TL;DR

TagGAN is a novel GAN-based framework that generates pixel-level disease maps from image-level labels, improving interpretability and aiding radiologists in medical image analysis without requiring detailed annotations.

Contribution

It introduces a weakly-supervised method for fine-grained disease localization using GANs, eliminating the need for pixel-level annotations during training.

Findings

Outperforms existing models in disease pixel identification on benchmark datasets.

Generates interpretable disease maps to visualize lesions.

Reduces radiologists' workload by automating mask generation.

Abstract

Precise identification and localization of disease-specific features at the pixel-level are particularly important for early diagnosis, disease progression monitoring, and effective treatment in medical image analysis. However, conventional diagnostic AI systems lack decision transparency and cannot operate well in environments where there is a lack of pixel-level annotations. In this study, we propose a novel Generative Adversarial Networks (GANs)-based framework, TagGAN, which is tailored for weakly-supervised fine-grained disease map generation from purely image-level labeled data. TagGAN generates a pixel-level disease map during domain translation from an abnormal image to a normal representation. Later, this map is subtracted from the input abnormal image to convert it into its normal counterpart while preserving all the critical anatomical details. Our method is first to generate fine-grained disease maps to visualize disease lesions in a weekly supervised setting without requiring pixel-level annotations. This development enhances the interpretability of diagnostic AI by providing precise visualizations of disease-specific regions. It also introduces automated binary mask generation to assist radiologists. Empirical evaluations carried out on the benchmark datasets, CheXpert, TBX11K, and COVID-19, demonstrate the capability of TagGAN to outperform current top models in accurately identifying disease-specific pixels. This outcome highlights the capability of the proposed model to tag medical images, significantly reducing the workload for radiologists by eliminating the need for binary masks during training.