Conference on Artificial Intelligence in Medical Imaging

Comparative Evaluation of Foundation Models versus End-to-End Trained Task-Specific Convolutional Models for Breast Implant Detection in Mammography

• Vasisht Ishwar, Emory University HITI Lab
• Frank Li, PhD; Young Seok Jeon, PhD; Beatrice Brown-Mulry, MSCS; Rohan Satya-Isaac, MS; Mohammadreza Chavoshi, MD; Hari Trivedi, MD; Judy Gichoya, MD, MS, FSIIM

 

Evaluating Vision-Language Foundation Models for Brain MRI Sequence Classification, Stability, and Metadata Harmonization

• Satvik Tripathi, Perelman School of Medicine at University of Pennsylvania
• Arnold Campbell, PhD

 

Foundation Model-Based Semi-Supervised Multiple Instance Learning for Automated Barrett's Esophagus Dysplasia Classification

• Amirali Khosravi, MD, Mayo Clinic
• Jaidip M. Jagtap, PhD; Bradley J. Erickson, MD, PhD, CIIP, FSIIM; Prasad G. Iyer, MD, FACG; Chamil Codipilly, MD

 

Promptable 3D Lesion Segmentation in CT: Converting 2D PACS Annotations into 3D Training Datasets

• Samuel D. Church, MS, University of Wisconsin – Madison
• Joshua D. Warner, MD, PhD, CIIP; Danyal Maqbool, MS; Xin Tie, PhD; Junjie Hu, PhD; Meghan G. Lubner, MD, FSAR, FACR; Tyler J. Bradshaw, PhD

 

Revolutionizing AI in Radiology — Evaluating Open-source versus Proprietary LLMs: Deepseek versus GPT-4o on Radiological Images

• Christopher Z. Liu, UCI School of Medicine
• Shawn H. Sun, MD; Divya Balchander, MD; Roozbeh Houshyar, MD

 

Vector Contours to the Rescue: How Basic Shape Outlines Keep Vision-LLM Generated Reports Clinically Grounded

• Krish Malik, Parkland High School
• Vedant Malik