5 papers
From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification
Di Zhang, Li Zhang, Jiashuai Liu +9
Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-languag…
Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis
Yucheng Xing, Ling Huang, Pei Liu +4
Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across…
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
Zhihui Chen, Kai He, Qingyuan Lei +4
Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing def…
Med-Banana: Learning Quality-Controlled Medical Image Editing from Success-and-Failure Trajectories
Zhihui Chen, Qingyuan Lei, Kai He +2
Text-guided medical image editing must satisfy the requested pathology while preserving anatomy, modality-specific appearance, and clinical plausibility. However, existing datasets…
Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
Yucheng Xing, Pei Liu, Jingying Ma +6
Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer…