Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration

TL;DR

This paper introduces a novel framework for long-tailed medical diagnosis that combines relation-aware representation learning with iterative classifier calibration, significantly improving classification performance on imbalanced datasets.

Contribution

The paper proposes a new LMD framework with RRL and ICC schemes to enhance feature representation and classifier calibration for long-tailed medical image classification.

Findings

Outperforms state-of-the-art methods on three public datasets

Effectively balances minority and majority class recognition

Improves diagnostic accuracy for rare diseases

Abstract

Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sample imbalance among different diseases leads algorithms biased to the majority categories, leading to poor performance for rare categories. Existing works formulated this challenge as a long-tailed problem and attempted to tackle it by decoupling the feature representation and classification. Yet, due to the imbalanced distribution and limited samples from tail classes, these works are prone to biased representation learning and insufficient classifier calibration. To tackle these problems, we propose a new Long-tailed Medical Diagnosis (LMD) framework for balanced medical image classification on long-tailed datasets. In the initial stage, we develop a Relation-aware Representation Learning (RRL) scheme to boost the representation ability by encouraging the encoder to capture intrinsic semantic features through different data augmentations. In the subsequent stage, we propose an Iterative Classifier Calibration (ICC) scheme to calibrate the classifier iteratively. This is achieved by generating a large number of balanced virtual features and fine-tuning the encoder using an Expectation-Maximization manner. The proposed ICC compensates for minority categories to facilitate unbiased classifier optimization while maintaining the diagnostic knowledge in majority classes. Comprehensive experiments on three public long-tailed medical datasets demonstrate that our LMD framework significantly surpasses state-of-the-art approaches. The source code can be accessed at https://github.com/peterlipan/LMD.