5 papers
Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning
Lubin Gan, Jing Zhang, Xinming Zhang +3
Whole-slide images may contain coexisting lesions, yet weak supervision typically provides only the most severe diagnosis. We formulate this setting as dominant-label multiple inst…
ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis
Lubin Gan, Jing Zhang, Heng Zhang +4
Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation models and advanced sequence modeling…
SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Images
Lubin Gan, Xiaoman Wu, Jing Zhang +4
Multiple instance learning (MIL) has become the leading approach for extracting discriminative features from whole slide images (WSIs) in computational pathology. Attention-based M…
SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution
Xiaoman Wu, Lubin Gan, Siying Wu +3
Multi-contrast Magnetic Resonance Imaging super-resolution (MC-MRI SR) aims to enhance low-resolution (LR) contrasts leveraging high-resolution (HR) references, shortening acquisit…
Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment
Lubin Gan, Jing Zhang, Linhao Qu +3
The fine-grained classification of brain tumor subtypes from histopathological whole slide images is highly challenging due to subtle morphological variations and the scarcity of a…