16 citations · 19 across the 4 of their papers we have counts for
8 papers · 1 filter
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Mingxi Fu, Jiawen Li, Renao Yan +4
Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology. However, existing MIL aggregators are still typically…
Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
Jiawen Li, Jiali Hu, Qiehe Sun +6
The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. T…
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.…