Label-efficient Hybrid-supervised Learning for Medical Image Segmentation

TL;DR

This paper introduces a novel hybrid-supervised learning framework for medical image segmentation that effectively utilizes limited strong labels and abundant weak labels, addressing instance inconsistency issues to achieve high performance with minimal annotation effort.

Contribution

The proposed framework considers individual weakly-annotated instances and guides their learning using gradient information from strongly-annotated instances, introducing the dynamic instance indicator and co-regularization techniques.

Findings

Achieves comparable segmentation performance with only 10% strong labels.

Effectively leverages weak labels to improve segmentation accuracy.

Demonstrates robustness across multiple datasets.

Abstract

Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to performance degradation. To address this problem, we propose a novel label-efficient hybrid-supervised framework, which considers each weakly-annotated instance individually and learns its weight guided by the gradient direction of the strongly-annotated instances, so that the high-quality prior in the strongly-annotated instances is better exploited and the weakly-annotated instances are depicted more precisely. Specially, our designed dynamic instance indicator (DII) realizes the above objectives, and is adapted to our dynamic co-regularization (DCR) framework further to alleviate the erroneous accumulation from distortions of weak annotations. Extensive experiments on two hybrid-supervised medical segmentation datasets demonstrate that with only 10% strong labels, the proposed framework can leverage the weak labels efficiently and achieve competitive performance against the 100% strong-label supervised scenario.