  Over the past decades, the incidence of thyroid cancer has been increasing
globally. Accurate and early diagnosis allows timely treatment and helps to
avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both
transverse and longitudinal views using thyroid ultrasound. However, the
appearance of the thyroid gland and lesions can vary dramatically across
individuals. Identifying key diagnostic information from both views requires
specialized expertise. Furthermore, finding an optimal way to integrate
multi-view information also relies on the experience of clinicians and adds
further difficulty to accurate diagnosis. To address these, we propose a
personalized diagnostic tool that can customize its decision-making process for
different patients. It consists of a multi-view classification module for
feature extraction and a personalized weighting allocation network that
generates optimal weighting for different views. It is also equipped with a
self-supervised view-aware contrastive loss to further improve the model
robustness towards different patient groups. Experimental results show that the
proposed framework can better utilize multi-view information and outperform the
competing methods.
