Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network on machine learning
Join MaLMIC at ImNO 2023
Thursday/Friday, March 23/24, 2023
MaLMIC is pleased to support three sessions at the ImNO 2023 symposium in London ON on Friday, March 24:
- 4 oral presentations starting at 1:50 p.m.
- 9 pitch presentations starting at 2:50 p.m.
- Lena Maier-Hein’s keynote presentation on “Embracing Failure” starting at 3:50 p.m.
You can review the full program here
You can register for the symposium at https://imno.ca/2023/registration%20and%20accomodation
Join MaLMIC at ImNO 2023
MaLMIC is pleased to support three sessions at the ImNO 2023 symposium in London ON on Friday, March 24:
- 4 oral presentations starting at 1:50 p.m.
- 9 pitch presentations starting at 2:50 p.m.
- Lena Maier-Hein’s keynote presentation on “Embracing Failure” starting at 3:50 p.m.
You can review the full program here.
You can register for the symposium at https://imno.ca/2023/registration%20and%20accomodation
Full Schedule
13:50 – 14:50 | Oral 11 – Deep Learning- Ballroom Centre
Chairs: Ryan Au and Amir Moslemi
O11-1: Predicting Tumour Mutational Burden from H&E Slides of Lung Squamous Cell Carcinoma: Observers vs a Neural Network
– Salma Dammak, Western University
O11-2: Improved Surgical Margin Detection in Mass Spectrometry Data Using Uncertainty Estimation
– Ayesha Syeda, Queen’s University
O11-3: Deep Learning for Prostate Cancer Recurrence Prediction on T2W MR Images
– Negin Piran Nanekaran, University of Guelph
O11-4: Deep Learning-Enabled Fluorescence Imaging for Surgical Guidance: Optical Phantoms from Patient Imaging
– Stefanie Markevich, University Health Network
14:50 – 15:20 | Pitch 11 – Deep Learning – Ballroom Centre
Chairs: Ryan Au and Wenchao Han
P11-1: Federated Learning for Kidney Tumor Segmentation: Preliminary Findings
– Zachary Szentimrey, University of Guelph
P11-2: MRI-degad: Conversion of Gadolinium-Enhanced T1w MRIs to Non-Contrast-Enhanced MRIs Using a Convolutional Neural Network
– Feyifoluwa Ogunsanya, Western University
P11-3: Artifact Detection Algorithm Using Deep Learning in Fetal MRI
– Adam Lim, Toronto Metropolitan University
P11-4: Background Parenchymal Enhancement Estimation on DCE Breast MRI using a Siamese Network
– Grey Kuling, University of Toronto
P11-5: Placental MRI Segmentation Using a Novel Convolutional Neural Network
– Alejo Costanzo, Toronto Metropolitan University
P11-6: Intracranial Hemorrhage Detection Using Machine Learning
– Navkiran Sohal, Western University
P11-7: Creating Better Whole Slide Image Datasets: Quality Control Detection of Out-Of-Focus Patches in Digital Pathology
– Phoenix Wilkie, University of Toronto
P11-8: Classifying Points of Interest in FAST Ultrasound Videos Using Neural Networks
– Ilan Gofman, University of Toronto
P11-9: Improving the Reliability of Video-Based Skill Assessment Metrics with Uncertainty Quantification
-Catherine Austin, Queen’s University
15:20 – 15:50 | Meet-and-Greet – Ballroom West
Poster Viewing (Pitch Sessions 11 & 12 presenting)
Coffee Break
15:50 – 16:35 | Keynote Session II – Ballroom Centre and East
Chairs: Sule Karagulleoglu Kunduraci and Jessica Rodgers
Embracing Failure
– Lena Maier-Hein, German Cancer Research Center