Conference on Artificial Intelligence in Medical Imaging

Adaptive Multi-Loss Learning with Self-Supervised Fine-Tuning for Robust Humerus Segmentation on Radiographs 

• Anthony Wu, MS, University of California, Irvine 
• Arya Amirhekmat, MD; Pooya Khosravi, MS; Paramveer Birring; Jason Liang; Maryam Golshan-Momeni, MD; Peter Chang, MD; Roozbeh Houshyar, MD; James Learned, MD 

 

Beyond Binary Masks: Stochastic Ensembles for Uncertainty-Aware Tumor Segmentation 

• Andres Guerrero, UCI Center for Applied Artificial Intelligence Research 
• Peter Chang, MD 

 

Development and Validation of Novel Two-stage Vascular Segmentation Model for Interventional Angiography 

• Michael Kovalchick, PhD Candidate, Wayne State University 
• Chad Klochko, MD; Hyeok Jun Lee; Hani Nasr; Hemal Dholakia; Abid Khan; Kundan Thind, PhD 

 

FewSAMNet - A Hybrid SAM-CNN Framework for Semi-Supervised Few-Shot Segmentation and Multi-Institutional Generalization 

• Chetana Krishnan, PhD Student, University of Alabama at Birmingham 
• Ezinwanne Onuoha; Alex Hung; Kyung H. Sung; Harrison Kim 

 

Radiomic Sampling: A Model-Free Approach to Enhance Diversity of Validation Datasets 

• Alexander Knapp, Cincinnati Children's 
• Jonathan Dudley, PhD; Hailong Li, PhD; Jonathan Dillman, MD, MSc; Elan Somasundaram, PhD 

 

Turning Noise into Signal: Interpretable Disagreement Profiling for Improved AI-Based 3D Tumor Segmentation 

• Fabian Umeh, MSc, Teesside University 
• Monika Pytlarz, MSc; MingDe Lin, PhD; Nazanin Maleki, MD; Raisa Amiruddin, MBBS; David Weiss; Khaled Bousabarah; Marina Ivory, MD, PhD; Mohamed Ghonim, MD; Mohanad Ghonim, MD; Albara Alotaibi, MD; Melisa Guelen, MD, PhD; Nathan Page, MD; Pascal Fehringer; Sedra Mhana; Bojan Petrovic, MD; Fatima Memon, MD, MPH; Basimah Albalooshy, MD; Elizabeth Schrickel, MD; Justin Cramer, MD; Michael Veronesi, MD, PhD; Spyridon Bakas, PhD; Mariam Aboian, MD, PhD