Medical Imaging with Deep Learning Tutorial 2020 - Joseph Paul Cohen
Joseph Paul Cohen

medical-imaging-deep-learning-tutorial-2020 (7 files)
medical-imaging-deep-learning-tutorial-2020-slides.pdf 7.74MB
Medical Imaging Tutorial 2020 - Ch0 - Intro.mp4 4.82MB
Medical Imaging Tutorial 2020 - Ch1 - Radiology and Multi-View.mp4 13.98MB
Medical Imaging Tutorial 2020 - Ch2 - Histology and Segmentation.mp4 15.75MB
Medical Imaging Tutorial 2020 - Ch3 - Cell Counting.mp4 10.11MB
Medical Imaging Tutorial 2020 - Ch4 - Incorrect Feature Attribution.mp4 10.64MB
Medical Imaging Tutorial 2020 - Ch5 - GANs in Medical Imaging.mp4 13.83MB
Type: Course
Tags: radiology

title= {Medical Imaging with Deep Learning Tutorial 2020 - Joseph Paul Cohen},
keywords= {radiology},
author= {Joseph Paul Cohen},
abstract= {This tutorial 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).

Presented by:
Joseph Paul Cohen PhD
Postdoctoral Fellow
Mila, University of Montreal

View presentations online here:},
terms= {},
license= {},
superseded= {},
url= {}

10 day statistics (16 downloads)

Average Time 13 mins, 50 secs
Average Speed 92.60kB/s
Best Time 0 mins, 34 secs
Best Speed 2.26MB/s
Worst Time 1 hrs, 30 mins, 28 secs
Worst Speed 14.16kB/s