Participants should be aware of the following financial/non-financial relationships:
Kenneth B. Bader, PhD: Disclosure information not submitted.
Learning Objectives:
Understand the relevance of machine learning to ultrasound imaging
Understand how to implement machine learning methods and interpret results
Implement machine learning in application to image guided therapies
Presentations:
10:00 AM – 10:21 AM
Implementation of Machine Learning for Beamforming
10:21 AM – 10:42 AM
Choices of Machine Learning Architectures
10:42 AM – 10:54 AM
Quantitative Ultrasound Texture-Derivative Methods Combined with Advanced Machine-Learning for Therapy Response Prediction: Method Development and Evaluation
10:54 AM – 11:06 AM
Radiomic Signatures Using Quantitative Ultrasound Integrated with Machine Learning for Monitoring Clinical Response in Patients with Head and Neck Carcinoma Treated with Radiotherapy
11:06 AM – 11:18 AM
Breast Lesion Characterization using Quantitative Ultrasound (QUS) and Derivative Texture Methods
11:18 AM – 11:30 AM
A Novel Paradigm for Quantitative Noninvasive Texture Analysis of Breast Masses in Ultrasound Images
