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Description: This lecture will be styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks. These methods will be covered in terms of architecture and objective function design. Also, a discussion about incorrect feature attribution and approaches to mitigate the issue. Prerequisites: basic knowledge of computer vision (CNNs) and machine learning (regression, gradient descent).
Topics:
- Chapter 1 – Radiology and Multi-View
- Chapter 2 – Histology and Segmentation
- Chapter 3 – Cell Counting
- Chapter 4 – Incorrect Feature Attribution
- Chapter 5 – GANs in Medical Imaging
Presentation given:
- CHIL Conference Tutorial on Medical Imaging with Deep Learning (July 2020)
- cs2541 ML4Healthcare at UToronto/Vector institute (Feb 2020)