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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.

Spring 2023 Biomedical Engineering E4460 section 001
Deep Learning in Biomedical Imaging
Deep Learning in BME / Im

Call Number 12079
Day & Time
R 1:10pm-3:40pm
326 Uris Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Andrew Laine
Method of Instruction In-Person
Course Description Introduction to methods in deep learning, with focus on applications to quantitative problems in biomedical imaging and Artificial Intelligence (AI) in medicine. Network models: Deep feedforward networks, convolutional neural networks and recurrent neural networks. Deep autoencoders for denoising. Segmentation and classification of biological tissues and biomarkers of disease. Theory and methods lectures will be accompanied with examples from biomedical image including analysis of neurological images of the brain (MRI), CT images of the lung for cancer and COPD, cardiac ultrasound. Programming assignments will use tensorflow / Pytorch and Jupyter Notebook. Examinations and a final project will also be required.
Web Site Vergil
Department Biomedical Engineering
Enrollment 52 students (65 max) as of 11:07AM Friday, March 31, 2023
Subject Biomedical Engineering
Number E4460
Section 001
Division School of Engineering and Applied Science: Graduate
Open To Engineering:Undergraduate, Engineering:Graduate, GSAS
Campus Morningside
Section key 20231BMEN4460E001

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SIS update 03/31/23 11:07    web update 03/31/23 11:23