Multi-Modal Information Fusion for Classification of Kidney Abnormalities

Being able to predict the outcome of a treatment has obvious utility in treatment planning. Retrospective studies investigating the correlation of various tumor morphological characteristics to the treatment outcomes are becoming increasingly feasible due to data collection and advances in machine learning. For renal cancers, computed tomography (CT) imaging is a widely used diagnostic modality owing to its highly discernible visible features. However, manual inspection of several CT images are quite labour-intensive and often subjective. To automate this task, we propose an attention-based deep learning framework that automatically analyzes renal tumors by fusing both the clinical and imaging features. We demonstrate its effectiveness on the 2022 Knight challenge.