Our doctoral student Metin SAĞIK successfully defended his thesis titled “DEEP FEATURE GENERATION FRAMEWORK FOR MEDICAL DIAGNOSTIC CLASSIFICATION: FROM UNIMODAL TO MULTIMODAL APPROACHES” on July 10, 2026. This study, carried out under the guidance of his thesis advisor Assoc. Prof. Dr. Abdurrahman GÜMÜŞ, addresses an ensemble-based Deep Feature Generation (DFG) framework that combines features obtained from transformer-based pre-trained models and transforms them into compact, discriminative representations using Iterative Neighborhood Components Analysis (INCA); it also aims to evaluate this framework in different clinical fields such as hydatid cyst and Alzheimer’s disease, and to develop a multimodal fusion architecture that integrates MRI, PET, and X-ray images with clinical text reports via large language models. We sincerely congratulate Metin SAĞIK on this achievement and wish him continued success in his future career.