Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acquisition styles. In this work, we introduce two new datasets, BreastMammo and DenseMammo, to facilitate robust multi-view mammography researc...
Hong-Yi Pan, Gorkem Durak, H. Aktas et al.· 0 citations
Mixture-of-experts (MoE) models combine specialized predictors under learned routing, offering a principled mechanism for leveraging heterogeneity in medical data. We present a hierarchical multimodal MoE for interstitial lung disease (ILD) classification that integrates a frozen, pre-trained imaging expert with struct...
A. Peltekian, Gorkem Durak, H. Aktas et al.· 0 citations
The results show strong performance on colonoscopy images and competitive performance on prostate MRI, supporting gaze-guided prototype refinement as a promising approach for segmentation-label-efficient, human-in-the-loop medical image segmentation.
Mohammed Oussama Benyahia, Marouane Tliba, M. A. Kerkouri et al.· 0 citations
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate A...
Gorkem Durak, H. Aktas, Tugba Akinci D'Antonoli et al.· Diagnostic and Interventiona...· 0 citations
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