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20232026
most citedAgent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis

16 citations · 21 across the 28 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

eess.IV2024

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…

eess.IV2024★ 1 cited

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…

cs.CV2024

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…

cs.CV2024★ 16 cited

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…

cs.CV2024★ 3 cited

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…

cs.CV2024

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.…

eess.IV2024

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…

cs.CV2024

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…

cs.CV2024

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…