4 citations · 17 across the 12 of their papers we have counts for
11 papers · 1 filter
PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization
Honglin Li, Zhongyi Shui, Yunlong Zhang +2
Computational pathology and whole-slide image (WSI) analysis are pivotal in cancer diagnosis and prognosis. However, the ultra-high resolution of WSIs presents significant modeling…
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Honglin Li, Yunlong Zhang, Pingyi Chen +3
Histopathology Whole Slide Image (WSI) analysis serves as the gold standard for clinical cancer diagnosis in the daily routines of doctors. To develop computer-aided diagnosis mode…
Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis
Honglin Li, Yusuan Sun, Chenglu Zhu +8
Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image…
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Yuxuan Sun, Yunlong Zhang, Yixuan Si +7
Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Sl…
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification
Yunlong Zhang, Honglin Li, Yunxuan Sun +4
Multiple Instance Learning (MIL) effectively analyzes whole slide images but faces overfitting due to attention over-concentration. While existing solutions rely on complex archite…
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Yuxuan Sun, Hao Wu, Chenglu Zhu +11
The emergence of large multimodal models has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their…
Benchmarking PathCLIP for Pathology Image Analysis
Sunyi Zheng, Xiaonan Cui, Yuxuan Sun +7
Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pretraining (CLIP) model h…
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Honglin Li, Yunlong Zhang, Chenglu Zhu +3
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole Slide Image (WSI) of histop…
Test-Time Training for Semantic Segmentation with Output Contrastive Loss
Yunlong Zhang, Yuxuan Sun, Sunyi Zheng +3
Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To i…
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
Zhongyi Shui, Yunlong Zhang, Kai Yao +7
Nucleus instance segmentation in histology images is crucial for a broad spectrum of clinical applications. Current dominant algorithms rely on regression of nuclear proxy maps. Di…
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
Yunlong Zhang, Honglin Li, Yuxuan Sun +3
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances,…