# Imbalanced Classification in Medical Imaging via Regrouping

Source: [Imbalanced Classification in Medical Imaging via Regrouping](<https://www.sota2.com/research/paper/68fa6dc51e209131cea3c074>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:43:12.182Z

Authors: Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun

Published: 2022-10-21T20:25:20.000Z

## Abstract

We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural idea can substantially boost the classification performance as measured by average precision (approximately area-under-the-precision-recall-curve, or AUPRC), which is more appropriate for evaluating imbalanced classification than other metrics such as balanced accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.12234>)

[PDF](<https://arxiv.org/pdf/2210.12234.pdf>)