Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance

TL;DR

This paper introduces a novel polynomial loss function based on Pade approximation combined with asymmetric sampling to improve classification of under-represented classes in long-tailed medical image datasets, addressing class imbalance issues.

Contribution

It proposes a new loss function tailored for long-tailed medical image classification, integrating Pade approximation and asymmetric sampling techniques.

Findings

Improved classification accuracy on under-represented classes.

Effective handling of class imbalance in medical datasets.

Open-source implementation available for reproducibility.

Abstract

Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss functions such as cross-entropy and binary cross-entropy are often inadequate due to their inability to address the imbalances between the classes with high representation and the classes with low representation found in medical image datasets. We introduce a novel polynomial loss function based on Pade approximation, designed specifically to overcome the challenges associated with long-tailed classification. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. We conducted extensive evaluations on three publicly available medical datasets and a proprietary medical dataset. Our implementation of the proposed loss function is open-sourced in the public repository:https://github.com/ipankhi/ALPA.