Week 3 – Histopathology and image analysis


June 25th at 12:00-13:30 EDT

Karl Otto, Phd

Experiences in medical software entrepreneurship

I will talk about my experiences creating, protecting and licensing novel software algorithms in the radiation therapy industry.  I’ll include some specific examples of the challenges I faced in bringing a high impact technology (VMAT) from an idea into routine clinical practice.

Joshua Giambattista, MD

Building and deploying AI models for clinical practice

I will talk about my experiences creating, protecting and I will talk about my experience founding Limbus AI including important lessons learned from building, validating and deploying clinical AI software. The talk will focus on the importance of clinician lead validation and domain expertise required to build successful expert grade AI models. I will describe experience with research collaboration and highlight the role of communication between clinicians and engineers necessary to make clinical AI safe and successful.

Carter Kolbeck, M.Sc.

Medical imaging machine learning pipelines for production

Building and maintaining end-to-end machine learning pipelines for clinical medical imaging applications.

Jon Giambattista, B.A.S.c

Shipping AI software in a regulated medical device industry

The challenges of deploying AI software in the regulated medical device industry. The differences in market regulations (Canada, US, Europe). Validation of AI to the standard of regulators.

Workshop 3 – Histopathology and semantic segmentation

June 27th at 10:00-11:30 EST

Instructor: Luca Weishaupt

The fundamentals of medical image analysis using the example of histopathological images. This workshop will walk participants through how to retrieve medical images from public datasets and how to use algorithmic tools to perform medical imager analysis and transformation.

Instructor: Dr. Farhad Maleki 

Evaluation of machine learning models 

A sound evaluation of the performance of machine learning models is essential for developing generalizable machine learning models with the capacity to be deployed in real-world settings. In this talk, we cover the best practices for evaluating machine learning models in the medical domain and the requirements for developing generalizable solutions in the healthcare domain. Read more  at: https://doi.org/10.1016/j.nic.2020.08.004

Instructor: Yujing Zou

Brief intro to histopathological images

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