Chenglu Zhu
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    • Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
    • PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
    • ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control
    • Unleashing the Power of Prompt-Driven Nucleus Instance Segmentation
    • WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
    • PathUp: Patch-wise Timestep Tracking for Multi-class Large Pathology Image Synthesising Diffusion Model
    • Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
    • Context-Aware Text-Assisted Multimodal Framework for Cervical Cytology Cell Diagnosis and Chatting
    • DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection
    • PathAsst: Generative Foundation AI Assistant for Pathology
    • DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection
    • Gradient-aware learning for joint biases: Label noise and class imbalance
    • Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology
    • PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
    • Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
    • WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
    • Assessing the Robustness of Deep Learning-Assisted Pathological Image Analysis Under Practical Variables of Imaging System
    • Attention-challenging multiple instance learning for whole slide image classification
    • Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
    • Long-MIL: scaling long contextual multiple instance learning for histopathology whole slide image analysis
    • Semi-supervised Cell Recognition under Point Supervision
    • Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification
    • Test-Time Training for Semantic Segmentation with Output Contrastive Loss
    • Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
    • Automatic and accurate segmentation of peripherally inserted central catheter (PICC) from chest X-rays using multi-stage attention-guided learning
    • Benchmarking the robustness of deep neural networks to common corruptions in digital pathology
    • Category Separation For Weakly Supervised Multi-Class Cell Counting
    • Chrsnet: Chromosome straightening using self-attention guided networks
    • Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning
    • End-to-end cell recognition by point annotation
    • Weakly Supervised Classification using Multi-Level Instance-Aware Optimization on Cervical Cytologic Image
    • Weakly supervised learning for cell recognition in immunohistochemical cytoplasm staining images
    • Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images
    • Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images
    • Generalizing nucleus recognition model in multi-source ki67 immunohistochemistry stained images via domain-specific pruning
    • Automatic centerline extraction of cerebrovascular in 4D CTA based on tubular features
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WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering

Jan 1, 2025ยท
Pingyi Chen
,
Chenglu Zhu
,
Sunyi Zheng
,
Honglin Li
,
Lin Yang
ยท 0 min read
Cite DOI
Type
Book section
Publication
Computer Vision – ECCV 2024
Last updated on Jan 1, 2025
← Unleashing the Power of Prompt-Driven Nucleus Instance Segmentation Jan 1, 2025
PathUp: Patch-wise Timestep Tracking for Multi-class Large Pathology Image Synthesising Diffusion Model Oct 1, 2024 →

ยฉ 2025 Me. This work is licensed under CC BY NC ND 4.0

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