Hasib Zunair / হাসিব জুনায়ের

I am currently a MASc. student, working on computer vision and machine learning with applications in medical imaging advised by Prof. A. Ben Hamza at Concordia University. I also recieved the MITACS Accelerate Fellowship to work jointly with Concordia and Décathlon Canada on semi-supervised learning for visual recognition using large-scale sport image data. Previously, I was a deep learning software engineer at Think Bricks LLC where I worked on Smart Retina, a medical software to analyze fundus images. I also developed open-source robots at The Tech Academy!

During my undergrad at North South University in Bangladesh, I was first exposed to research when I was advised by Dr. Nabeel Mohammed. I served as the founding chair of IEEE Robotics and Automation Society (RAS) Student Branch Chapter which is the first RAS student chapter in Bangladesh.

I like to cook fancy meals (beauty is in the eye of the beholder ;)) and play competitive e-sports, Dota 2 and CSGO, during my free time. If you're into esports, add me on Steam! I also like funny dog videos! 🐕

Email  /  LinkedIn  /  GitHub  /  Google Scholar  /  Twitter  /  Medium

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My work involves building artificial intelligence (AI)-based computer vision systems from planning, development, to deployment. I have experience in coding up end-to-end deep learning workflows such as semi-supervised/ transfer/ adversarial learning for classification, detection, segmentation and generation of 2D images as well as 3D (videos, CT & MRI scans). Recently I have been working on developing data-efficient deep learning techniques which can reduce the negative effects of severe class imbalance, and also those which do not depend heavily on intensive manual labeling efforts. During my undergrad, I built 😬 a lot of other random stuff over time.

ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans
Authors : Hasib Zunair, Aimon Rahman, and Nabeel Mohammed
Conference and Labs of the Evaluation Forum (CLEF), Under Review, 2021

Paper / Code / Leaderboard (2nd place)

We pretrain a model on videos for human activity recognition which leads to better representations for the downstream tuberculosis type classification task, especially for under-represented class samples. Our method achieved 2nd place in the ImageCLEF 2021 Tuberculosis Type Classification Challenge.

MoNuSAC2020: A Multi-organ Nuclei Segmentation and Classification Challenge
Authors : Ruchika Verma, Neeraj Kumar,... Hasib Zunair, A. Ben Hamza and others.
IEEE Transactions on Medical Imaging (TMI), 2021 (Joint Paper)
ISBI MoNuSAC Workshop, 2020 (Oral Presentation)

Paper / Code / Slides / Video (Time - 1:58:46)

Automating the tasks of detecting, segmenting, and classifying cell nuclei can free up the pathologists’ time for higher value tasks and reduce errors due to fatigue and subjectivity. This paper summarizes and publicly releases the challenge dataset, and compile key findings of the methods developed by various participants.

Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
Authors : Hasib Zunair and A. Ben Hamza
Social Network Analysis and Mining, 2021

Paper / Code

Propose the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images using unsupervised domain adaptation by leveraging class conditioning and adversarial training.

A Comparative Analysis of Deep Learning Architectures on High Variation Malaria Parasite Classification Dataset
Authors : Aimon Rahman, Hasib Zunair, Tamanna Rahman Reme, M Sohel Rahman, M.R.C.Mahdy
Tissue and Cell, 2021


Transformed a high variation malaria localization dataset into a malaria classification dataset. Several classification algorithm benchmarks are provided for the task of classifying presence of malaria from microscopic images of isolated red blood cells.

Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
Authors : Hasib Zunair, Aimon Rahman, Nabeel Mohammed, and Joseph Paul Cohen
(Oral Presentation)

Paper / Code / Video

Showed that analyzing 3D medical images in a per slice basis is a sub-optimal approach, that can be improved by 3D context. Ranked 5-th in ImageCLEF 2019.

Melanoma Detection using Adversarial Training and Deep Transfer Learning
Authors : Hasib Zunair and A. Ben Hamza
Physics in Medicine and Biology, 2020

Paper / Code / Demo (Try it out!)

Improved classification performance by synthesizing under-represented class samples from the over-represented ones. Synthetic samples are used as additional training data to reduce class imbalance.

Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss
Authors : Labib Chowdhury, Hasib Zunair and Nabeel Mohammed
Applied Sciences, 2020

Paper / Code

SincNet models based on joint angular margin loss not only consistently outperformed current prior models, but also generalizes well on unseen and diverse tasks such as Bengali speaker recognition.

Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks
Authors : Aimon Rahman, Hasib Zunair, M Sohel Rahman, Jesia Quader Yuki, Sabyasachi Biswas, Md Ashraful Alam, Nabila Binte Alam, M.R.C. Mahdy
arXiv, 2019

Paper / Code

Benchmarked several classification algorithms for the task of detecting malaria from microscopic images of red blood cells. Transfer learning approach worked best in our study.

Estimating Severity from CT Scans of Tuberculosis Patients using 3D Convolutional Nets
Authors : Hasib Zunair, Aimon Rahman, Nabeel Mohammed
Conference and Labs of the Evaluation Forum (CLEF), 2019

Paper / Code

A 3D CNN with a slice selection method employed in the task of chest CT image analysis for predicting tuberculosis (TB). Our method achieved 10-th place in the ImageCLEF 2019 Tuberculosis SVR - Severity scoring.

Unconventional Wisdom: A New Transfer Learning Approach Applied to Bengali Numeral Classification
Authors : Hasib Zunair, Nabeel Mohammad, Sifat Momen
International Conference on Bangla Speech and Language Processing (ICBSLP), 2018
(Oral Presentation)

Paper / Code

An accuracy of 97.09% was achieved on the NumtaDB Bengali handwritten digit dataset, which was obtained by freezing intermediate layers.

Design and Implementation of an Automated Web Based Multifunctional Attendance System
Authors : Hasib Zunair, Oishi Maniha, Jubayer Kabir
International Conference on Smart Sensors and Applications (ICSSA), 2018
(Oral Presentation, Best Paper)

Paper / Slides

Implementation of an automated multifunctional attendance system which uses rfid, fingerprint, and real time facial recognition.

Design and Implementation of an IOT based Monitoring System for Inland Vessels using Multiple Sensor Network
Authors : Hasib Zunair, Wordh Ul Hasan, Kimia Tuz Zaman, Irfanul Haque, Soumic Shekhar Aoyon
International Conference on Smart Sensors and Applications (ICSSA), 2018
(Oral Presentation)

Paper / Slides

A wireless sensor network with a real time web application for monitoring multiple ships to prevent catastrophic events due to overloading.

Design and Implementation of Security Patrol Robot using Android Application
Authors : Tahzib Mashrik, Hasib Zunair, Maofic Farhan Karin
Asia Modelling Symposium (AMS), 2017
(Oral Presentation)


A low-cost autonomous mobile security robot based on a multisensor system for the purpose of sending alarms remotely.

Open Source

AI Dermatology Assistant (Try it out!)

AI Model Code / Web App Code / REST API Code

This is a demonstration of a full stack deep learning project from training a model to deploying it, using a REST API endpoint as well a separate end-user prototype web application. The model is built for predicting the presence of melanoma from dermoscopic skin lesions using neural networks.

3D Image Classification from CT Scans
Keras Official Documentation, 2020

Code / Video by Henry AI Labs

Train a 3D convolutional neural network to predict presence of pneumonia.

Res-U-Net architecture for reconstruction of high resolution knee MRI scans
Team: Hasib Zunair and Aimon Rahman
fastMRI Image Reconstruction Challenge (Single coil track), Facebook AI Research, 2019


Trained a U-Net architecture with a pretrained ResNet backbone on knee MRIs at the slice level. The goal was to reconstruct high resolution images from the given undersampled image.

Boss Detector using Facial Recognition


Change monitor screen when your boss is coming towards you!

Pynotify: A Python package to send emails like humans

A Python package built to send email notifications which can be integrated into any existing piece of python software.


Teaching Assistant (Lab Demonstrator), COMP 478 Winter 2021

Teaching and assisting graduate students with implementation of image processing algorithms and course projects.

Instructor, Image Classification with Python and Keras, 2019.
Instructor, Introduction to Python Programming, 2019
Instructor, Image Processing and Computer Vision, 2018

Technical workshops where participants get to know about programming in Python, image processing fundamental using OpenCV and building image classification models using Keras.

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Last updated June 2021.