6 citations · 19 across the 13 of their papers we have counts for
13 papers · 1 filter
NuNext: Reframing Nucleus Detection as Next-Point Detection
Zhongyi Shui, Honglin Li, Xiaozhong Ji +7
Nucleus detection in histopathology is pivotal for a wide range of clinical applications. Existing approaches either regress nuclear proxy maps that require complex post-processing…
CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logic
Yuxuan Sun, Yixuan Si, Chenglu Zhu +5
Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance lear…
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
Yuxuan Sun, Yixuan Si, Chenglu Zhu +7
The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and wh…
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…
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…
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…
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,…
Masked conditional variational autoencoders for chromosome straightening
Jingxiong Li, Sunyi Zheng, Zhongyi Shui +9
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists…
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
Yuxuan Sun, Chenglu Zhu, Sunyi Zheng +6
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, of…
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification
Honglin Li, Chenglu Zhu, Yunlong Zhang +5
While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification, such a paradigm still faces performance and generali…
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
Yunlong Zhang, Yuxuan Sun, Honglin Li +3
When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an e…