5 papers · 1 filter
Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis
Pei Liu, Xiangxiang Zeng, Tengfei Ma +3
Whole-Slide Images (WSIs) are widely used for estimating the prognosis of cancer patients. Current studies generally follow a cancer-specific learning paradigm. However, the availa…
Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification
Jiaxiang Gou, Luping Ji, Pei Liu +1
Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention…
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology
Pei Liu, Luping Ji, Jiaxiang Gou +2
Histopathology Whole-Slide Images (WSIs) provide an important tool to assess cancer prognosis in computational pathology (CPATH). While existing survival analysis (SA) approaches h…
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
Pei Liu, Luping Ji, Xinyu Zhang +1
Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classificatio…
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
Rui Yang, Pei Liu, Luping Ji
Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classi…