通过数据为中心策略优化乳腺癌全切片成像中的淋巴细胞检测
Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging through Data-Centric Strategies
AI总结：
本文提出以数据为中心的优化流程，通过数据集增强策略，在不修改YOLOv5架构的情况下提升乳腺癌全切片图像中淋巴细胞检测性能，并展示其泛化潜力。
摘要：
在组织病理学切片中高效且精确地量化淋巴细胞，对于表征肿瘤微环境和洞察免疫治疗反应至关重要。我们开发了一种以数据为中心的优化流程，利用现成的YOLOv5模型，无需任何架构修改即可获得优异的淋巴细胞检测性能。我们的贡献依赖于战略性数据集增强策略，包括针对组织图像独特性质定制的新型生物学上采样和自定义视觉凝聚变换，从而显著提升模型性能。我们的优化揭示了一个关键认识：在密集定制的情况下，标准计算病理学模型能够实现高能力的生物标志物开发，而无需增加架构复杂性。我们在乳腺癌背景下展示了该方法的价值，我们的策略带来了良好的淋巴细胞检测性能，呼应了一种具有广泛影响力的范式转变。此外，我们的数据整理技术使得关键的组织学分析基准得以实现，突显了增强的泛化潜力。
英文摘要：
Efficient and precise quantification of lymphocytes in histopathology slides is imperative for the characterization of the tumor microenvironment and immunotherapy response insights. We developed a data-centric optimization pipeline that attain great lymphocyte detection performance using an off-the-shelf YOLOv5 model, without any architectural modifications. Our contribution that rely on strategic dataset augmentation strategies, includes novel biological upsampling and custom visual cohesion transformations tailored to the unique properties of tissue imagery, and enables to dramatically improve model performances. Our optimization reveals a pivotal realization: given intensive customization, standard computational pathology models can achieve high-capability biomarker development, without increasing the architectural complexity. We showcase the interest of this approach in the context of breast cancer where our strategies lead to good lymphocyte detection performances, echoing a broadly impactful paradigm shift. Furthermore, our data curation techniques enable crucial histological analysis benchmarks, highlighting improved generalizable potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
