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Unlocking adaptive digital pathology through dynamic feature learning
Jiawen Li, Tian Guan, Qingxin Xia +17
Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quanti…
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Xitong Ling, Yuanyuan Lei, Jiawen Li +5
Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images…
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image
Jiawen Li, Qiehe Sun, Renao Yan +7
With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer d…
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation
Qilai Zhang, Jiawen Li, Peiran Liao +4
The two primary types of Hematoxylin and Eosin (H&E) slides in histopathology are Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen (FF). FFPE slides offer high quality hist…
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification
Hongbo Chu, Qiehe Sun, Jiawen Li +5
Histopathological whole slide image (WSI) analysis with deep learning has become a research focus in computational pathology. The current paradigm is mainly based on multiple insta…
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Jiawen Li, Yuxuan Chen, Hongbo Chu +4
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as in…