My research interests lies at the intersection of computer vision and machine learning,
with the goal of building human-level computer vision systems that can understand, model, and recreate
the visual world around us. I am working on algorithms for
visual perception, synthesis
(object recognition, segmentation, generation, ...) and
representation learning (pre-training networks with strong, weak,
or no supervision, ...) to
reduce the need for intensive manual labeling efforts. 🧠 👀
I have a passion for AI, automation, open-source software and teaching. I have contributed code to Keras
that demonstrates a deep learning workflow on
3D Image Classification from CT Scans,
which also got featured in a
video by Henry AI Labs. I've also built some
Pynotify: A Python package to send emails and
Boss Detector: Changes monitor screen when your boss is near 😬 .
I support Slow Science.
Designing Efficient Deep Learning Models for Computer-Aided Medical Diagnosis
Authors: Hasib Zunair
Masters Thesis, 2021
Annotating medical image data is time consuming, costly and error prone, and
the scarcity of labeled data limits the effectiveness of supervised learning.
This thesis introduces methods to address the scarcity of labels as well as
class imbalance which results in a bias towards to over-represented class for tasks
such as classification, binary and multi-class semantic segmentation.
Sharp U-Net: Depthwise convolutional network for biomedical image segmentation
Authors : Hasib Zunair, A. Ben Hamza
Computers in Biology and Medicine, 2021
Sharp U-Net achieves improved performance on six medical image segmentation datasets
in both binary and multi-class segmentation tasks while adding no extra learnable parameters.
The idea is instead of applying a plain skip connection,
a depthwise convolution of the encoder feature map with a sharpening kernel filter is done prior to merging the
encoder and decoder features.
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification
Decathlon Docs /
Blog Post /
Authors : Hasib Zunair, Yan Gobeil, Samuel Mercier, A. Ben Hamza
ACM Workshop on Multimedia Content Analysis in Sports, 2021
An efficient semi-supervised learning method is proposed for binary and multi-class image classification.
The method requires 6x less compute time and 5x less memory compared to prior arts. We also show that our method
boosts robustness of visual classification models, even without specifically optimizing for adversarial robustness.
MoNuSAC2020: A Multi-organ Nuclei
Segmentation and Classification Challenge
Slides / Video (Time - 1:58:46) /
Leaderboard (11th place)
Authors : Ruchika Verma, Neeraj Kumar, Hasib Zunair, A. Ben Hamza and others.
IEEE Transactions on Medical Imaging (TMI), 2021
ISBI MoNuSAC Workshop, 2020 (Oral Presentation)
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.
Automatic segmentation of blood cells from microscopic slides: A comparative analysis
Authors : Deponker Sarker Depto, Shazidur Rahman, Md. Mekayel Hosen, Mst Shapna Akter,
Tamanna Rahman Reme, Aimon Rahman, Hasib Zunair, M Sohel Rahman, M.R.C.Mahdy
Tissue and Cell, 2021
This work proposes a blood cell segmentation dataset consisting of multiple cell types.
Additionally, all cell types
do not have equal instances, which encourages researchers to develop algorithms for
learning from imbalanced classes in a few shot learning paradigm. We also provide both learning and non-learning based
methods as baselines.
Monocular-to-3D Virtual Try-On using Deep Residual U-Net
Project Page /
Authors : Hasib Zunair
COMP 6381 Digital Geometric Modelling, Fall 2021
Res-M3D-VTON is a pipeline for monocular to 3D virtual try-on (VTON)
for fashion clothing which uses residual learning to synthesize correct clothing parts,
preserve logo of clothing and reduce artifacts to finally output better textured
3D try-on meshes.
Dermatology Assistant (Try it out!)
AI Model Code / Web App Code / REST API Code
Authors : Hasib Zunair
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
Thyroid nodule segmentation from Ultrasound (US) images
Authors: Hasib Zunair, Tajwar Abrar Aleef, Aimon Rahman and Labib Chowdhury
MICCAI TN-SCUI Challenge, 2020
In this work, several state-of-the-art image segmentation techniques were explored for optimizing
Thyroid Nodule segmentation from Ultrasound images. This inlcuded supervised learning, transfer learning,
and generative adversarial learning.
Lab Demonstrator, COMP 6771: Image Processing, Winter 2022
Lab Demonstrator, COMP 333: Intro to Data Analytics, Fall 2021
Lab Demonstrator, COMP 6771: Image Processing, Winter 2021
Technical Support Specialist, Ericsson AI/ML Upskill Program 2021-2022
Presenting tutorials to Ericsson employees relevant to the projects as well as
suggesting relevant software tools for the projects. Also support project implementations
and ensure correctness of project approaches and results.
Student Instructor, Image Classification with Python and Keras, Fall 2019
Student Instructor, Introduction to Python Programming, Winter 2019
Student Instructor, Image Processing and Computer Vision, Fall 2018
Template stolen from Jon Barron. Thanks for dropping by.
Last updated January 2022.