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MonMIND 2026
MonMIND 2026
(May 1st, 2026, Salon des diplômés, **ETS Montreal**)
What: Montreal Medical Imaging Workshop
When: May 1, 2026, 9:30am-4:30pm
Where: ETS Montreal, Salon des diplômés, Pavillon E,
1220 R. Notre Dame O, Montréal, QC H3C 1K6
About MonMIND: Montreal Medical Imaging Networking Day, MonMIND serves as a dynamic hub for students and trainees to showcase their research and engage in high-level scientific exchange with peers and experts. Its primary goal is to spark interdisciplinary collaborations that bridge the gap between technical imaging innovation and real-world clinical applications.
Lunch and Coffee will be provided
Schedule 2026
09:30 - introduction + coffee
10:00 - Student Session #1 (4x (10min presentations + 5min Q&A))
Liam O'Connor – VIVIE: Virtual Reality Simulation for External Ventricular Drain Placement Training
Joshua Castillo – Markerless Catheter Tracking for Real-Time Augmented Reality Guided Ventriculostomy
Pascal Spiegler – Towards user-centered interactive medical image segmentation in VR with an assistive AI agent
Soroush Javadi – Learning to Guide: Multimodal Trajectory Forecasting of Expert Sonographers
11:00 - Break
11:15 - Keynote: Polina Golland - Real-Time Navigation in Diagnostic Fetal Brain MRI
12:15 - Lunch
13:15 - Student Session #2 (4x (10min presentations + 5min Q&A))
Matthew Toews – Multi-Atlas keypoint alignment for localizing 3D brain MRI landmarks
Nairouz Shetata – Shape Analysis using Graph Neural Networks in Medical Imaging
Lucas Mercier – Context-aware 3D gait pose estimation via lightweight multi-scale features sampling
Faizan Jilani – Resolving Brain Degeneration Differences between Alzheimer’s disease Progressors and Non-progressors using Tensor-based Morphometry
14:15 - Break
14:30 - Student Session #3 (4x (10min presentations + 5min Q&A))
Ghazal Danaee – Evaluation of Performance Disparities in Deep Learning Models for Neuroimaging and the Role of Entropy-Based Active Learning
Taha Koleilat – MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
Fereshteh Shakeri – Boosting Vision-Language Models for Histopathology Classification: Predict all at once
Mohamed Amine Elforaici – Multimodal Approach for Prognostic Modeling in Colorectal Liver Metastases
15:30 - Social Activity
16:30 - End of event
Keynote 2026 – Prof. Polina Golland, MIT
Real-Time Navigation in Diagnostic Fetal Brain MRI
MIT/CSAIL
This talk will present our current work towards self-driving fetal brain MRI. Interpretation of fetal brain MRI continues to be a challenge due to fetal motion even though fast single shot techniques such as T2-weighted Half Fourier Single-shot Turbo spin-Echo (HASTE) is used. Due to motion, the radiologist must interpret multiple stacks of imperfect HASTE images in varying oblique orientations without the ability to cross-correlate across stacks. To mitigate motion, we propose to dynamically adjust the imaging plane to follow the fetal head during the scan. To achieve this goal, we introduce fast, low-resolution 3D echo-planar imaging volumetric navigators (EPI-vNavs) that are interleaved with the 2D anatomical images. We estimate the fetal head pose using a novel equivariant neural architecture combined with an overcomplete rotation representation that explicitly captures object symmetries. While existing pose estimation methods struggle to generalize to navigator volumes due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and spin-history artifacts, our approach captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. We present experimental results that demonstrate the promise of our approach to achieve high diagnostic quality fetal head MRI and discuss potential generalizations to other high-motion scenarios.
Joint work with Ramya Muthukrishnan (MIT), Benjamin Billot (INRIA), Elfar Adalsteinsson (MIT), Borjan Gagoski (BCH), and P. Ellen Grant (BCH).
Polina Golland is a Sunlin (1966) and Priscilla Chou professor of Electrical Engineering and Computer Science at MIT and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). She received her PhD in 2001 from MIT and her Bachelor and Masters degrees in 1993 and 1995 from Technion, Israel. Polina's primary research interest is in developing novel machine learning and geometric techniques for medical image analysis and understanding. With her students, Polina has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. She has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis. Polina is currently on the editorial board of the Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions (MICCAI) and of the American Institute for Medical and Biological Engineering (AIMBE).
We thank the financial support of the Canada Research Chair program and itechsanté
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