Session 2: Artificial Intelligence in Imaging

Session 2: Artificial Intelligence in Imaging
Call for Abstracts / Research Papers:

Sub-Tracks: Artificial Intelligence in Radiology, Medical Imaging, Radiologic Diagnostics, CT, MRI, X-Ray, Ultrasound, PET, Imaging Techniques, Quantitative Image Analysis, Machine Learning, Deep Learning, Diagnostic Imaging, Radiologists, Imaging Specialists, Tele-Radiology, Scanner Technology, Imaging Data Interpretation, Radiology AI Systems

Radiology AI (Artificial Intelligence)

A radiology AI system is software designed to assist radiologists in interpreting medical imaging or provide automated diagnostic support. Its main function is to leverage machine learning and image analysis to interpret radiologic images. Tasks such as generating diagnostic reports, detecting anomalies, segmenting anatomical structures, or predicting disease outcomes can be learned from data using machine learning. Key techniques include decision trees, random forests, and deep learning.

Deep learning has significantly advanced AI in radiology by enabling systems to detect complex imaging features that traditional algorithms cannot capture. Deep learning networks can mimic expert radiologist performance by learning intricate visual patterns directly from imaging data. Implementation of these systems requires large datasets and considerable computational resources, making it a cutting-edge area in diagnostic medicine.

Who Should Attend?

Radiologists, Radiology Residents, Imaging Scientists, Clinical Scientists in Radiology, Consultants, Trainee Radiologists, Medical Students interested in Radiology, PhD students & post-doctoral researchers in imaging sciences, Biomedical Scientists, Physicians, Clinical Practitioners, Medical Education Professionals, Laboratory Managers and Supervisors, Radiologic Technologists, Ultrasound Technicians, Imaging Analysts, Oncologists, Surgeons, and all professionals involved in medical imaging and diagnostics.