Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

TL;DR

This paper introduces LeFeD, a semi-supervised learning method that leverages decoder feature discrepancies to improve medical image segmentation, outperforming existing methods without complex additional techniques.

Contribution

Proposes LeFeD, a novel SSL approach that uses decoder feature discrepancies as feedback to enhance learning in medical image segmentation.

Findings

LeFeD outperforms eight SOTA methods on three datasets.

Discrepancy in decoder features is a valuable learning signal.

LeFeD achieves new state-of-the-art results in semi-supervised segmentation.

Abstract

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that inconsistent decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancy obtained from two decoders, by feeding the discrepancy as a feedback signal to the encoder. The core design of LeFeD is to enlarge the difference by training differentiated decoders, and then learn from the inconsistent information iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles such as uncertainty estimation and strong constraints, as well as setting a new state-of-the-art for semi-supervised medical image segmentation. Code is available at \textcolor{cyan}{https://github.com/maxwell0027/LeFeD}