In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based not only on the images but also on a variety of clinical records. This means that pathologists examine medical images with some prior knowledge of the patients and that the attention regions may change depending on the clinical records. In this study, we propose a method called the Personalized Attention Mechanism (PersAM), by which the attention regions in medical images are adaptively changed according to the clinical records. The primary idea of the PersAM method is to encode the relationships between the medical images and clinical records using a variant of Transformer architecture. To demonstrate the effectiveness of the PersAM method, we applied it to a large-scale digital pathology problem of identifying the subtypes of 842 malignant lymphoma patients based on their gigapixel whole slide images and clinical records.
翻译:在医学图像诊断中,确定受关注地区,即受诊断关注地区是一项重要任务。已经开发了各种方法,从给定的医疗图像中自动确定目标地区。然而,在实际医疗实践中,诊断不仅基于图像,而且还基于各种临床记录。这意味着病理学家在病人事先了解一些病理的情况下检查医疗图像,关注地区可能根据临床记录而改变。在这项研究中,我们提出了一个称为个性关注机制(PersAM)的方法,根据临床记录,医疗图像中的受关注地区将适应性地改变。PersAM方法的主要理念是使用变异体结构对医疗图像和临床记录之间的关系进行编码。为了证明 PersAM方法的有效性,我们将它应用于一个大规模的数字病理学问题,即根据他们的GHApixel整个幻灯片图像和临床记录来识别842位恶性淋病病人的亚型。