16 citations · 20 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Xitong Ling, Minxi Ouyang, Yizhi Wang +8
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist path…
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification
Mingxi Ouyang, Yuqiu Fu, Renao Yan +5
Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of…
Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification
Xuenian Wang, Shanshan Shi, Renao Yan +4
In the field of whole slide image (WSI) classification, multiple instance learning (MIL) serves as a promising approach, commonly decoupled into feature extraction and aggregation.…