Hyperbolic Space Feature Enhancement and Interaction Mechanism for Classification on Small Multimodal Medical Datasets

ABSTRACT Cancer is one of the leading causes of death worldwide, and early diagnosis of the disease is one of the most important factors in reducing mortality or increasing lifespan. Traditionally, healthcare experts use various sources of information to determine a diagnosis, often including some form of imaging along with clinical and demographic data. In this work, we propose a method to improve fusion of medical images and multi‐field complementary data for classification in small datasets using deep learning models. To achieve that, we introduce a novel complementary data extraction block using hyperbolic space feature enhancement by Poincaré transformation and a mechanism for multi‐field feature interactions. We evaluate it using datasets for the diagnosis of skin cancer (PAD‐UFES‐20) and oral cavity cancer (NDB‐UFES). The experimental results show statistically significant improvement in performance on PAD‐UFES‐20 dataset of the proposed model over baseline. The SingleS Poincaré Conv–Concat with MetaBlock fusion model using PiT image backbone achieved performance of in balanced accuracy metric. For NBD‐UFES dataset, the ViT architecture with SingleDS Poincaré Conv–Concat using MetaBlock fusion () obtained the best mean among all the results evaluated. The results indicate the feasibility of the proposed approach. The source code is public available at GitHub at https://github.com/lmlima/Hyperbolic‐space‐feature‐enhancement‐and‐interaction‐mechanism‐for‐classification .