In-depth radiological and explainability analysis of deep learning

Mayo Clinic Innovations in Gastroenterology and Hepatology 2026: Artificial Intelligence and Beyond2026

Cite this work

Halil Ertugrul Aktas, Andrea Mia Bejar, Ziliang Hong, Rutger Hendrix, Hongyi Pan, Elif Keles, Fergan Bol, Yavuz Bahadir Taktak, Yuri Velichko, Alpay Medetalibeyoglu, Sukru Mehmet Erturk, Lili Zhao, Frank H. Miller, Michael Wallace, Rajesh N. Keswani, Gorkem Durak, and Ulas Bagci (2026). In-depth radiological and explainability analysis of deep learning. Mayo Clinic Innovations in Gastroenterology and Hepatology 2026: Artificial Intelligence and Beyond

BibTeX

Cite this work

Related publications

Improved segmentation of polyps and visual explainability analysis

In-depth radiological and explainability analysis of deep learning predictions for IPMN risk stratification on MRI

A multi-center MRI study with comprehensive radiological analysis of IPMN risk stratification with explainable AI

Uncertainty-aware explainable AI for pancreatic cysts: identifying deep learning vulnerabilities and ensuring safe clinical triage in IPMN management

Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations