Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection

TL;DR

This paper demonstrates that using GANs for data augmentation in histopathology can significantly improve tumor detection accuracy by addressing data scarcity and imbalance issues.

Contribution

The study introduces a GAN-based data augmentation approach to enhance tumor detection in histopathology images, reducing data imbalance problems.

Findings

Data augmentation with GANs increased tumor detection from 80% to 87.5%.

Augmented dataset with 50% more images improved model performance.

GAN-generated images helped mitigate data scarcity in rare cancer types.

Abstract

Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major problem that stands in the way is lack of data for a few cancer-types. In this paper, we ascertain that data augmentation using GANs can be a viable solution to reduce the unevenness in the distribution of different cancer types in our dataset. Our demonstration showed that a dataset augmented to a 50% increase causes an increase in tumor detection from 80% to 87.5%