Portrait of Rohan Banerjee

Rohan Banerjee

rohan.banerjee@mila.quebec
Collaborating Alumni - Polytechnique Montréal
Supervisor
Guillaume Lajoie
Research Topics
Computer Vision
Deep Learning
Medical Machine Learning
Website
Google Scholar
GitHub

Publications

A unified 12-lead ECG-language model for interpretation and clinical-endpoint prediction
Rohan Banerjee
Juliette Beaulieu
Nicolas Dostie
B Mondesert
Ram Ahuja
Alexis Nolin-Lapalme
Gilbert Jabbour
Shreya Shree Srikanth
Guillaume Marquis‐Gravel
Olivier Tastet
A. Sowa
Julia Cadrin‐Tourigny
Jacques Delfrate
Robert Avram
Automated electrocardiogram (ECG) interpretation has advanced, yet most systems remain narrow classifiers that emit fixed labels rather than… (see more) the narratives or endpoint-specific answers clinicians need. Generative approaches could instead produce rich narratives, but are constrained by the gap between continuous biosignals and discrete language tokens. Here we present DeepECG-Tok, which reframes ECG interpretation as a unified instruction-following problem. A residual vector-quantization tokenizer (QINCo) maps 12-lead waveforms to language-model-compatible tokens. Its frozen embeddings achieved a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.96 for 77-condition classification, outperforming supervised and self-supervised baselines. Aligned with a large language model, a single instruction-tuned model performed ECG interpretation, structured reporting and clinical endpoint prediction, including left ventricular ejection fraction, structural heart disease and atrial fibrillation risk, using 7.27 million question–answer pairs. Frozen-tokenizer diagnostic classification transferred without retraining to two external cohorts, retaining macro-averaged AUROCs of 0.88–0.90, and clinical-endpoint prediction transferred to two further cohorts (external LVEF ≤40% AUROC 0.74–0.76). Evaluated end to end using an ontology-grounded large language model as a judge, which achieved a mean agreement (Cohen’s κ) of 0.82 against two cardiologists, the unified instruction-tuned model scored 0.71 internally and 0.50–0.53 in the same external cohorts. In a blinded reader study, board-certified cardiologists and residents rated its free-text reports comparably to reference clinician reports, with a paired win–tie–loss distribution of 32:35:33 and a forced-choice preference of 0.53 among decided cases, with no significant difference between the model and reference reports in either comparison. These findings establish discrete ECG tokenization as a foundation for general-purpose models that generate clinically useful interpretations and answer diverse questions directly from cardiac waveforms.
2026-07-23
medRxiv (accepted)
doi.org
EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data
Rohan Banerjee
Merve Kaptan
Alexandra Tinnermann
Ali Khatibi
Alice Dabbagh
Christian Büchel
Christian W. Kündig
Christine Law
Dario Pfyffer
David J. Lythgoe
Dimitra Tsivaka
Dimitri Van De Ville
Falk Eippert
Fauziyya Muhammad
Gary H. Glover
Gergely David
Grace Haynes
Jan Haaker
J.C. Brooks
Jürgen Finsterbusch … (see 21 more)
Katherine T. Martucci
Kimberly J. Hemmerling
Mahdi Mobarak-Abadi
Mark A. Hoggarth
Matthew A. Howard
Molly G. Bright
Nawal Kinany
Olivia S. Kowalczyk
Patrick Freund
Robert Barry
Sean Mackey
Shahabeddin Vahdat
Simon Schading‐Sassenhausen
Stephen B. McMahon
Todd Parish
Véronique Marchand‐Pauvert
Yufen Chen
Zachary A. Smith
Kenneth A. Weber
Benjamin De Leener
Julien Cohen‐Adad
Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (see more)cessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.1 and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared with other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.
2024-12-31
Imaging Neuroscience (published)
doi.org