This paper explores the application of Human-in-the-Loop (HITL) strategies in training machine learning models in the medical domain. In this case a doctor-in-the-loop approach is proposed to leverage human expertise in dealing with large and complex data. Specifically, the paper deals with the integration of genomic data and Whole Slide Imaging (WSI) analysis of breast cancer. Three different tasks were developed: segmentation of histopathological images, classification of this images regarding the genomic subtype of the cancer and, finally, interpretation of the machine learning results. The involvement of a pathologist helped us to develop a better segmentation model and to enhance the explainatory capabilities of the models, but the classification results were suboptimal, highlighting the limitations of this approach: despite involving human experts, complex domains can still pose challenges, and a HITL approach may not always be effective.

翻译：本文探讨了在人机协同（Human-in-the-Loop, HITL）策略下，在医学领域中训练机器学习模型的应用。具体而言，本文提出了一种“医生参与验证”的方法，以借助人类专家在处理大规模复杂数据时的专业知识。本文重点研究了乳腺癌基因组数据与全切片影像（Whole Slide Imaging, WSI）分析的集成。我们开展了三项不同的任务：组织病理学图像的分割、基于癌症基因组亚型的图像分类，以及对机器学习结果的解读。病理学家的参与帮助我们构建了更优的分割模型，并增强了模型的可解释性，然而分类结果并未达到理想状态，这揭示了该方法的局限性：即便有人类专家参与，复杂领域仍可能带来挑战，且人机协同方法并不总是有效的。