  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.
