Digital Pathology

In Partnership with DeePathology Ltd.

Course Home

Course leadership

Chen Sagiv, PhD - DeePathology Ltd

How to contact us

  • The best way to get in contact with your course leadership is through this form. Once you fill out the contact request form with your email, one of the professors listed above will reach out to you directly with an answer to your question.

How to succeed in this course

  • Register, take the normal histology module.

  • For each module and finish with the digital pathology practicum (aka AI “wet” lab)

  • Read all material posted on the course website

  • Engage with other classmates and course leadership on Twitter with #DigiPathElective 

  • Do not hesitate to ask questions—we are here for you!

  • Enjoy your time learning about Digital Pathology!

Disclaimer:

Course materials have either been created specifically for PathElective.com or curated from free sources. When the course links to an outside source or link which is free, PathElective does not claim ownership or any rights to that material - we are merely promoting excellent pathology education with all due credit to original authors

Course Description

Digital pathology is a complex and developing field in pathology and laboratory medicine that is truly revolutionizing how we think about disease diagnosis and histopathologic tissue examination. In this course, you will explore the foundations of digital pathology and neural networks through the training sessions developed by Dr. Chen Sagiv of DeePathology, Ltd. By the end of this course, you should be able to explain the basis of neural networks in pathology as well as apply them to real-world situations and provide information about their utility in practice.

Technical help


Course Map

Lesson 1: Intro to Digital Pathology

Lesson 4: How good is your network in Machine Learning

Lesson 2: Neural Networks

Lesson 5: The Good, The Bad, and The Biased

Lesson 3: Ai & Computer Vision

Lesson 6: Ai in Pathology - Real Life Examples

Lesson 7: Data Annotation

Lesson 8: Validation, Visualization and Explainability

Lesson 9: Ai “Wet” Lab

 

Ready to start?