Nauman Bashir Bhatti
Deep Learning &
Experienced AI Researcher, interested in designing high-performance deep learning models that are understandable to humans. PhD candidate at York University, specializing in brain metastasis research and the development of a deep learning-based automatic brain tumor diagnostic framework. With a Master's degree in Computer Science and experience as a Research Assistant at the Medical Imaging and Diagnostics Lab (MIDL-NCAI), I have a strong background in Deep Learning, Computer Vision, and Medical Imaging. Currently, I am associated with the QUANTIMB Lab as a Graduate Researcher. Prior to that, I worked as a Research Assistant at the National Center for Artificial Intelligence in Pakistan.
Nov 2022 - Present
> Working on Brain Tumor/Metastasis
Jan 2022 - Sept 2022
> Developed an AI-based DICOM viewer for the diagnosis of Tuberculosis, Breast Cancer and Brain tumour. > Worked on fully automated AI-Based PACS Picture Archiving and Communication System). >Presented demo at Brain Oncology Symposium organized by Aga Khan Medical University. > Designed a Logo for MID Lab-NCAI. > Presented AI models and DICOM Viewer to hospitals and Labs. > Collected and preprocessed the data from hospitals. > As a member of the organizing team, organized AI industrial events and training sessions. > Assisted MS students with their thesis, project and publications.
Dec 2020 - Nov 2021
> Developed models for visual attribution of medical images, particularly for domains where pixel-level labels are difficult to attain (such as Alzheimer). Evaluated and performed experiments on three datasets including synthetic, Alzheimer;s disease Neuro imaging Initiative(ADNI) and, BraTS dataset. > Got hands-on experience in Generative Adversarial Networks, Zero-shot learning, EXplainable and interpretable deep learning methods, and Visual Feature Attribution techniques. Worked on several deep learning projects, including skin lesion detection, generated unseen objects via zero-shot learning, receptive field view, diabetic retinopathy, Mask R-CNN, YOLO, TbX11k, concept extraction and activation vectors , Alzheimer’s disease and Brain tumor abnormal to normal image conversion and vice versa.
Mar 2019 - Oct 2020
Designed and Deployed Real Estate Application focusing IoT based Solutions
November 2022
Thesis Title
In Progress
September 2021
Thesis Title
CNET: A Concept‑Controlled Deep Learning Architecture for
Interpretable Image Classification
September 2018
Thesis Title
READ: Requirements Engineering Analysis and Design
Bhatti, N. B., & Sadeghi-Naini, A. (2024, July). Small Lesions, Big Impact: An Automated Segmentation Framework for Brain Metastases. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-4). IEEE..
Zia, T., Bashir, N., Ullah, M. A., & Murtaza, S. (2022). SoFTNet: A concept-controlled deep learning architecture for interpretable image classification. Knowledge-Based Systems, 108066.
Tehseen Zia, Shakeeb Murtaza, Nauman Bashir Bhatti, David Windridge, Zeeshan Nisar, VANT-GAN: Adversarial Learning for Discrepancy-Based Visual Attribution in Medical Imaging, Pattern Recognition Letters, 2022, ISSN 0167-8655
Bashir, N., Bilal, M., Liaqat, M., Marjani, M., Malik, N., & Ali, M. (2021, March). Modeling Class Diagram using NLP in Object-Oriented Designing. In 2021 National Computing Colleges Conference (NCCC) (pp. 1-6). IEEE.
Associate Professor, York Research Chair, P.Eng, York University PI QUANTIMB Lab Toronto, Canada
Assistant Professor, COMSATS University Islamabad Co-PI Medical Imaging and Diagnostics LAB-NCAI Islamabad, Pakistan
Assistant Professor, COMSATS University Islamabad Co-PI Medical Imaging and Diagnostics LAB-NCAI Islamabad, Pakistan
Assistant Professor, COMSATS University Islamabad Islamabad, Pakistan
MCR, MSOL, CEO Buildingz Miami, FL, USA
Co-founder, CEO Inveitco Sydney, Austrailia
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