Reliability-Aware Semi-supervised Mutual Learning for Acute Ischemic Stroke Lesion Segmentation.

Abstract

For patients with acute ischemic stroke (AIS), rapid and accurate lesion localization is critical for improving treatment outcomes. However, automatic stroke lesion segmentation remains highly challenging due to the scarcity of large-scale annotated datasets. Recently, semi-supervised learning (SSL) has achieved remarkable progress in medical image segmentation, yet its performance is still hindered by unreliable pseudo-labels. To address this issue, we propose a novel SSL framework, termed reliability-aware mutual learning (RAML), which employs two subnetworks with a shared encoder, a primary decoder, and an auxiliary decoder. Specifically, RAML introduces uncertain region relearning (URR) regularization, which leverages prediction uncertainty from both subnetworks to identify and refine unreliable regions in labeled images. For unlabeled images, reliability-aware mutual pseudo-supervision (RMPS) regularization is designed to enable cross-supervision based on reliable pseudo-labels. Furthermore, feature difference learning (FDL) regularization is incorporated to promote prediction diversity across subnetworks. Experiments on two acute ischemic stroke datasets and the Left Atrium dataset demonstrate the effectiveness of the proposed RAML in semi-supervised segmentation tasks. The code for this project is available at https://github.com/EricMedimuist/RAML.