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

Annotation-efficient Semi-supervised and Active Learning for Breast Cancer Segmentation in DCE-MRI.

Accurate breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital for diagnosis and treatment planning. Despite advances in deep learning, its performance remains constrained by the need for extensive voxel-wise annotations. To mitigate this burden, we propose an annotation-e...

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

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...

Task-agnostic explainable contrastive learning model for malignant cell detection in urine cytology

Bladder cancer remains one of the most prevalent urological malignancies, where early detection is critical for improving patient outcomes. Urine cytology provides a non-invasive and cost-effective screening modality; however, the development of automated diagnostic systems is challenged by the scarcity of expert-annot...

DMFNet: a semi-supervised dual-path multiscale fusion network for lesion segmentation in breast ultrasound

Accurate lesion segmentation in breast ultrasound images is crucial for early diagnosis and treatment planning, yet remains highly challenging due to inherent imaging characteristics, including low signal-to-noise ratio, speckle noise artifacts, blurred boundaries, and significant inter-lesion appearance variability. F...

ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging

ProtoCAM is proposed, an explainable few-shot learning framework for breast lesion classification that integrates mask-guided feature encoding, prototypical metric learning, and gradient-based visual explanations to enable robust classification under limited training samples.