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English(EN) AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology

AtlasPatch方法利用基础模型加速病理学图像处理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AtlasPatch，一种用于计算病理学中高效处理全切片图像（WSI）的新方法。该方法利用基础模型（特别是SAM2的参数高效适应性）在缩略图级别检测组织，然后指导全分辨率下的切片生成。与现有的深度学习预处理技术相比，AtlasPatch速度显著提高，速度提升高达16倍，同时保持了下游分类任务的性能。 AI

影响 该方法可以加速大规模计算病理学工作流程，从而实现更快的研发和诊断。

排序理由 该集群描述了一篇研究论文，其中详细介绍了一种在特定科学领域中用于图像处理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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AtlasPatch方法利用基础模型加速病理学图像处理

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该集群描述了一篇研究论文，其中详细介绍了一种在特定科学领域中用于图像处理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ahmed Alagha, Christopher Leclerc, Yousef Kotp, Omar Metwally, Calvin Moras, Peter Rentopoulos, Ghodsiyeh Rostami, Bich Ngoc Nguyen, Jumanah Baig, Abdelhakim Khellaf, Vincent Quoc-Huy Trinh, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar, Mahdi S. Hosseini ·

    AtlasPatch：基于可扩展基础模型的计算病理学组织检测和切片提取

    arXiv:2602.03998v3 Announce Type: replace-cross Abstract: Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflows. Existing methods often rely either on t…

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