Kaleem Siddiqi

shubhamchopra2995@gmail.com

Shubham Chopra

Doctorat - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

elise.donszelmann-lund@mila.quebec

Elise Donszelmann-Lund

Maîtrise recherche - McGill

Sophie Ellwood

Maîtrise recherche - McGill

sophie.ellwood@mila.quebec

Doctorat - McGill

dmytro.fedorenko@mila.quebec

isabel.frolick@mila.quebec

Google Scholar

Isabel Frolick

Maîtrise recherche - McGill

Site web

marc.theveneau@mila.quebec

Ben Lo

Doctorat - McGill

benjamin.lo@mila.quebec

Megan Ng

Doctorat - McGill

megan.ng@mila.quebec

Marc Theveneau

Doctorat - McGill

Publications

Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex

Christopher K. Salmon

Tabish A. Syed

J. Benjamin Kacerovsky

Nensi Alivodej

Alexandra L. Schober

Tyler F.W. Sloan

Michael T. Pratte

Michael P. Rosen

Miranda Green

Adario Chirgwin-Dasgupta

Shaurya Mehta

Affan Jilani

Yanan Wang

Hojatollah Vali

Craig A. Mandato

Keith K. Murai

Using high-resolution serial electron microscopy datasets and computer vision, this study provides a systematic analysis of astrocytic nanoa… (voir plus)rchitecture from multiple samples of layer 2/3 of adult mouse neocortex, and presents quantitative evidence that astrocytes organize their morphology into purposeful, classifiable assemblies with unique structural and subcellular organelle adaptations related to their physiological functions.

2023-02-28

Current Biology (publié)

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI

Thierry L. Lefebvre

Ozan Ciga

Sahir Rai Bhatnagar

Yoshiko Ueno

Sameh Saif

Eric Winter-Reinhold

Anthony Dohan

Philippe Soyer

Reza Forghani

Jan Seuntjens

Caroline Reinhold

Peter Savadjiev

2023-02-28

Diagnostic and Interventional Imaging (publié)

Finger-STS: Combined Proximity and Tactile Sensing for Robotic Manipulation

Francois R. Hogan

Jean-François Tremblay

Bobak H. Baghi

Michael Jenkin

Gregory Dudek

This paper introduces and develops novel touch sensing technologies that enable robots to better sense and react to to intermittent contact … (voir plus)interactions. We present Finger-STS, a robotic finger embodiment of the See-Through-your-Skin (STS) sensor that can capture 1) an “in the hand” visual perspective of an object that is being manipulated and 2) a high resolution tactile imprint of the contact geometry. We demonstrate the value of the sensor on a Bead Maze task. Here the multimodal feedback provided by the Finger-STS is leveraged by a robot to locate a bead visually and to guide it across a wire in response to tactile cues, with no additional sensing or planning required. To achieve this, we introduce a set of relevant visuotactile operations using computer vision-based algorithms. In particular, we sense the proximity of the object relative to the sensor as well as the nature of contact as a high resolution stick/slip vector field tracking the object motion in the finger.

2022-07-17

IEEE Robotics and Automation Letters (publié)

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning

Arnab Kumar Mondal

Vineet Jain

Siamak Ravanbakhsh

2022-06-27

Proceedings of the 39th International Conference on Machine Learning (publié)

proceedings.mlr.press

Medial Spectral Coordinates for 3D Shape Analysis

Morteza Rezanejad

Mohammad Khodadad

Hamidreza Mahyar

Hervé Lombaert

Michael Gruninger

Dirk B. Walther

In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects repre-sented by surface meshes,… (voir plus) their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral co-ordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to iso-metric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.

2022-06-17

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

arxiv.org

Myofiber reconstruction at micron scale reveals longitudinal bands in heart ventricular walls

Drisya Dileep

Tabish A. Syed

Tyler F. W. Sloan

Perundurai S. Dhandapany

Minhajuddin Sirajuddin

The coordinated contraction of myocytes drives the heart to beat and circulate blood. Due to the limited spatial resolution of whole heart i… (voir plus)maging and the piecemeal nature of high-magnification studies, a confirmed model of myofiber geometry does not yet exist. Using microscopy and computer vision we report the first three-dimensional reconstruction of myofibers across entire mouse ventricular walls at the micron scale, representing a gain of three orders of magnitude in spatial resolution over the existing models. Our analysis reveals prominent longitudinal bands of fibers that are orthogonal to the well-known circumferential ones. Our discovery impacts present understanding of heart wall mechanics and electrical function, with fundamental implications for the study of diseases related to myofiber disorganization.

2022-05-11

bioRxiv (prépublication)

Neural correlates of local parallelism during naturalistic vision

John Wilder

Morteza Rezanejad

Sven Dickinson

Allan Jepson

Dirk B. Walther

Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this p… (voir plus)rocess. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.

2022-01-20

PLoS ONE (publié)

Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes

Peter Savadjiev

Benoit Gallix

Morteza Rezanejad

Sahir Bhatnagar

Alexandre Semionov

Reza Forghani

Caroline Reinhold

David H. Eidelman

Ronald J. Dandurand

Purpose To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chr… (voir plus)onic obstructive pulmonary disease (COPD) at chest CT. Materials and Methods In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy). Results Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images. Conclusion The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features.Keywords: Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021.

2021-12-14

Radiology: Artificial Intelligence (publié)