September 2022 - January 2023
Seminar Series
AI for Breast MRI
on Zoom every other Thursday 11AM ET
Objective
Deep Learning for Radiology is a rapidly developing research field with a large number of investigators in Breast Radiology alone. We are organizing an online seminar series entitled “AI for Breast MRI”. The goal is to keep up with the progress in this field all around the world.
Theme
“The failures that lead to the cutting edge.” It is easy enough to read a published paper, but a lot goes on behind the scenes. We are interested to hear of the problems that came up, which ultimately lead to breakthroughs, and of the problems that still remain.
Format
It will be different. We want to break away from the conventional 1h monologue with slides. We want to have conversations instead. We will be starting with 30 min presentations followed by conversations, breakout groups, exercises ... we will experiment with the format. We encourage questions during the presentation, and presenters will join the discussion in other session. The objective is to keep everyone engaged for 1h, making space for everyone, at all career levels (students, postdoc and researchers).
Time
These moderated online events will be open to all experts and trainees in the field and take place every two weeks at 11 AM Eastern Time. This time has been selected to make it accessible to participants around the world. We envision this seminar series to become a point of reference for the community across all time zones. The schedule is below.
The Speakers
Postdoctoral Research Fellow at NYU, Jan will present his latest results in deep learning for diagnosis of cancer in Breast MRI.
September 15th
Associate Professor in Radiology at Duke Center for AI in Radiology, Maciej will talk about data sharing for benchmarks and proper model development.
September 29th
Radiologist at Memorial Sloan Kettering, Liz will present her latest results in deep learning for radiologist-level segmentation on cancer in Breast MRI.
October 13th
Professor of bioinformatics at Mount Sinai, Li will present about training deep learning models with partially annotated and unannotated mammograms for breast cancer detection.
November 3rd
Radiologist and researcher at Memorial Sloan Kettering Cancer Center. Sarah will talk about the development of a Deep Learning tool for triaging breast cancer MRI .
November 17th
Radiologist and Director of Research at Memorial Sloan Kettering Cancer Center. Kaja will talk about the development of a Deep Learning tool for triaging breast cancer MRI
November 17th
Assistant Professor at UC Berkeley, Adam will talk about mammography screening policy and risk modeling.
December 8th
Docent and radiologist at Karolinska University Hospital (Sweden). Fredrik will present about an ongoing clinical study for AI tools for mammogram analysis and MRI screening benefits.
December 15th 11AM
Associate Professor in Radiology at the Imaging Research Division, University of Pittsburgh, Shandong will talk about breast MRI and background parenchymal enhancement
January 12th
Research Assistant Professor, Radiology, The University of Chicago. Heather will talk about harmonization and ethics/fairness in breast MRI, as well as merging multi-modality information
January 26th
Senior researcher at the BCN-MedTech Centre of the Universitat Pompeu Fabra, Barcelona. Karim will talk about trustworthy AI for MRI based estimation of treatment response to neoadjuvant chemotherapy in breast cancer
February 9th
Postponed: TBD
Postdoctoral researcher at University Medical Center Utrecht (Netherlands), Bas will talk about explainable AI in breast MRI.
February 23rd
Schedule
Sep 29 - Maciej Mazurowski
Oct 13 - Liz Sutton / Lukas Hirsch
Nov 3 - Li Shen
Nov 17 - Katja Pinker-Domenig/Sarah Eskreis-Winkler
Dec 8 - Adam Yala
Dec 15 - Fredrik Strand
Jan 12 - Shandong Wu
Jan 26 - Heather Whitney
Feb 9 - Karim Lekadir
Feb 23 - Bas Van der Velden
Organizers:
Liz Sutton, Yu (Andy) Huang - Memorial Sloan Kettering Cancer Center
Maciej Mazurowski, Lars Grimm - Duke University
Kelly Myers, Michael Jacobs - John Hopkins University
Lucas Parra, Hernán Makse, Lukas Hirsch - The City College of New York