Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification

TL;DR

This paper introduces a dual consistency framework combining absolute and relative location regularizations, along with contrastive learning, to improve semi-supervised medical image classification.

Contribution

It proposes a novel dual teacher framework that enforces both absolute and relative sample consistency, enhancing feature robustness and classification accuracy.

Findings

Outperforms existing semi-supervised methods on multiple datasets.

Effectively incorporates relative sample information for better generalization.

Uses contrastive learning to reduce noise in dense feature spaces.

Abstract

Semi-supervised learning (SSL) is a popular solution to alleviate the high annotation cost in medical image classification. As a main branch of SSL, consistency regularization engages in imposing consensus between the predictions of a single sample from different views, termed as Absolute Location consistency (AL-c). However, only AL-c may be insufficient. Just like when diagnosing a case in practice, besides the case itself, the doctor usually refers to certain related trustworthy cases to make more reliable decisions.Therefore, we argue that solely relying on AL-c may ignore the relative differences across samples, which we interpret as relative locations, and only exploit limited information from one perspective. To address this issue, we propose a Sample Consistency Mean Teacher (SCMT) which not only incorporates AL c but also additionally enforces consistency between the samples' relative similarities to its related samples, called Relative Location consistency (RL c). AL c and RL c conduct consistency regularization from two different perspectives, jointly extracting more diverse semantic information for classification. On the other hand, due to the highly similar structures in medical images, the sample distribution could be overly dense in feature space, making their relative locations susceptible to noise. To tackle this problem, we further develop a Sample Scatter Mean Teacher (SSMT) by utilizing contrastive learning to sparsify the sample distribution and obtain robust and effective relative locations. Extensive experiments on different datasets demonstrate the superiority of our method.