Gradient-Based Self-supervised Multi-task Optimization for Weakly Supervised Brain Tumor Segmentation.

Dynamic adaptation and boundary-aware learning for semi-supervised medical image segmentation.

DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...

BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentation

BaT-SAM is presented, a parameter-efficient adaptation framework that enables fully automatic brain tumor segmentation with the Segment Anything Model (SAM) and introduces a lightweight self-prompting box regressor that is jointly optimized with the adapter using scheduled sampling and predicts tumor locations directly...

Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation of brain tumor sub-regions across a heterogeneous patient population. The proposed method employs the nnU-Net framework with a large residual encoder architecture, int...

RMAU-Net: Dual-output supervision with lightweight multi-scale attention for liver tumor segmentation

SOTS: Leveraging Self-supervised Pretraining for Label-Efficient Ovarian Tumor Segmentation in Ultrasound Images.

A Self-supervised Ovarian Tumor Segmentation (SOTS) framework that leverages unlabeled ultrasound images to learn robust feature representations and achieves segmentation accuracy comparable to fully supervised U-Net variants while requiring substantially fewer annotations is proposed.

Uncertainty-guided decoupled complementary network for binary semi-supervised medical image segmentation

Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boun...