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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 Location |
R 1:10pm-3:40pm 326 Uris Hall |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Andrew Laine |
| Type | LECTURE |
| 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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