Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
Med Image Anal. 2023 Oct;89:102891. doi: 10.1016/j.media.2023.102891. Epub 2023 Jul 13.
Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.
病理学数字化已被提议作为大多数病理部门面临的严重人员配备危机的基本缓解策略。尽管有其好处，但由于成本和病理学家对主观图像质量的担忧，一些障碍阻止了数字工作流程的广泛采用。在这项工作中，我们根据空间和采样分辨率定量确定了用于对乳腺组织组织病理学图像进行二进制分类的最低图像质量要求。我们在公开数据集上训练深度学习分类器模型的集合，以获得基线准确性，并根据我们的理论模型对这些图像进行计算降级，以确定可接受诊断准确性所需的最小分辨率。我们的结果表明，图像可以在大多数商业全切片成像系统的分辨率以下显著降低，同时保持合理的准确性，这表明宏观特征足以用于对染色的乳腺组织进行二进制分类。一种能够识别不需要人工评估的健康组织的快速低成本成像系统可以作为一种分诊系统，以减少病例量并缓解当前劳动力的巨大压力。
