3 papers
cs.CV2025
Rethinking the Sample Relations for Few-Shot Classification
Guowei Yin, Sheng Huang, Luwen Huangfu +2
Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, lev…
cs.CV2024
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Wenhao Tang, Fengtao Zhou, Sheng Huang +3
Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL pa…
cs.CV2023
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Wenhao Tang, Sheng Huang, Xiaoxian Zhang +3
The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WS…