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In medical image diagnosis, identifying the attention region, i.e., theregion of interest for which the diagnosis is made, is an important task.Various methods have been developed to automatically identify target regionsfrom given medical images. However, in actual medical practice, the diagnosisis 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 knowledgeof the patients and that the attention regions may change depending on theclinical records. In this study, we propose a method called the PersonalizedAttention Mechanism (PersAM), by which the attention regions in medical imagesare adaptively changed according to the clinical records. The primary idea ofthe PersAM method is to encode the relationships between the medical images andclinical records using a variant of Transformer architecture. To demonstratethe effectiveness of the PersAM method, we applied it to a large-scale digitalpathology problem of identifying the subtypes of 842 malignant lymphomapatients based on their gigapixel whole slide images and clinical records.

Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records