Semi-Supervised Dual-Threshold Contrastive Learning for Ultrasound Image Classification and Segmentation

TL;DR

This paper introduces Hermes, a semi-supervised contrastive learning framework that improves ultrasound image classification and segmentation by addressing pseudo-label confidence issues and enabling task collaboration.

Contribution

Hermes combines contrastive and semi-supervised learning with inter-task attention and consistency strategies, advancing ultrasound image analysis.

Findings

Hermes outperforms state-of-the-art methods on multiple datasets.

The inter-task modules improve segmentation and classification accuracy.

The approach effectively reduces negative transfer between tasks.

Abstract

Confidence-based pseudo-label selection usually generates overly confident yet incorrect predictions, due to the early misleadingness of model and overfitting inaccurate pseudo-labels in the learning process, which heavily degrades the performance of semi-supervised contrastive learning. Moreover, segmentation and classification tasks are treated independently and the affinity fails to be fully explored. To address these issues, we propose a novel semi-supervised dual-threshold contrastive learning strategy for ultrasound image classification and segmentation, named Hermes. This strategy combines the strengths of contrastive learning with semi-supervised learning, where the pseudo-labels assist contrastive learning by providing additional guidance. Specifically, an inter-task attention and saliency module is also developed to facilitate information sharing between the segmentation and classification tasks. Furthermore, an inter-task consistency learning strategy is designed to align tumor features across both tasks, avoiding negative transfer for reducing features discrepancy. To solve the lack of publicly available ultrasound datasets, we have collected the SZ-TUS dataset, a thyroid ultrasound image dataset. Extensive experiments on two public ultrasound datasets and one private dataset demonstrate that Hermes consistently outperforms several state-of-the-art methods across various semi-supervised settings.