Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
本文针对计算病理学中AI的可解释性问题，通过定义术语、构建分类体系和提出任务驱动框架来解决XAI方法的一致性和临床应用挑战。
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From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction
研究针对肿瘤文档信息提取难题，提出并评估了一种名为nMAS的多代理系统工作流方法，实现临床相关结构化数据的高效提取。
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RT-DETRv2 Explained in 8 Illustrations
Current RT-DETRv2 architectures lack clear, systematic visual explanations, hindering interpretability and reproducibility. To address this, we propose the first hierarchical, structured diagrammatic framework—comprising eight original, meticulously designed illustrations—that systematically elucidates the end-to-end inference pipeline, encoder-decoder coordination, and core components including multi-scale deformable attention, with explicit tensor flow and modular logic. Our method integrates tensor-flow tracing, functional module decomposition, and geometrically grounded attention visualization, enabling the first full-stack explanatory rendering of RT-DETRv2. This work bridges a critical gap in deep visualization research for real-time object detection models. It substantially lowers the cognitive barrier to understanding, providing a reliable mental model for model analysis, debugging, and pedagogy—thereby facilitating broader adoption and advancement of real-time detection technologies.
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Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
本文针对计算病理学中AI的可解释性问题，通过定义术语、构建分类体系和提出任务驱动框架来解决XAI方法的一致性和临床应用挑战。
0 citationsRead paper
From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction
研究针对肿瘤文档信息提取难题，提出并评估了一种名为nMAS的多代理系统工作流方法，实现临床相关结构化数据的高效提取。
0 citationsRead paper
RT-DETRv2 Explained in 8 Illustrations
Current RT-DETRv2 architectures lack clear, systematic visual explanations, hindering interpretability and reproducibility. To address this, we propose the first hierarchical, structured diagrammatic framework—comprising eight original, meticulously designed illustrations—that systematically elucidates the end-to-end inference pipeline, encoder-decoder coordination, and core components including multi-scale deformable attention, with explicit tensor flow and modular logic. Our method integrates tensor-flow tracing, functional module decomposition, and geometrically grounded attention visualization, enabling the first full-stack explanatory rendering of RT-DETRv2. This work bridges a critical gap in deep visualization research for real-time object detection models. It substantially lowers the cognitive barrier to understanding, providing a reliable mental model for model analysis, debugging, and pedagogy—thereby facilitating broader adoption and advancement of real-time detection technologies.
0 citationsRead paper