Jin-Zhu Yang

LabCora: label correlation-aware semi-supervised multi-label medical image classification.

Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

KD-Brain is proposed, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process, which design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functio...

MDSNet: Exploring Refined Speech Representations for Screening Mental Disorders.

Speech-based mental disorder screening (SMS) is hindered by the scarcity of labeled training data. Although self-supervised learning (SSL) can alleviate this limitation by leveraging unlabeled speech, the general representations learned by existing SSL methods often contain unhelpful redundancies and are not well adapt...

Segmentation-synthesis co-training for semi-supervised domain generalizable medical image segmentation

ReMatch is introduced, a segmentation-synthesis co-training framework for semi-supervised domain generalization in medical image segmentation that Synthesizes unlabeled images with both high semantic consistency and style diversity by leveraging Texture and Shape features derived from the segmentation process.