One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric

TL;DR

This paper introduces a One Shot GANs approach to address the long tail problem in medical image datasets, specifically for skin lesion classification, by augmenting scarce classes and employing a new content space assessment metric.

Contribution

The paper proposes a novel One Shot GANs method combined with a new content space metric to improve minority class data augmentation in imbalanced medical datasets.

Findings

Effective augmentation of minority classes in HAM10000 dataset.

Improved detection accuracy for rare skin lesion classes.

Introduction of a novel content space assessment metric.

Abstract

Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies significantly, a problem emerges. Training on such imbalanced datasets can result in selective detection, where a model accurately identifies images belonging to the majority classes but disregards those from minority classes. This causes the model to lack generalizability, preventing its use on newer data. This poses a significant challenge in developing image detection and diagnosis models for medical image datasets. To address this challenge, the One Shot GANs model was employed to augment the tail class of HAM10000 dataset by generating additional samples. Furthermore, to enhance accuracy, a novel metric tailored to suit One Shot GANs was utilized.