Certified Artificial Intelligence Developer
Artificial Intelligence, Python, deep learning, and applied machine 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.
April 2023 - Present
> Collect and anonymize multi-modal imaging data in accordance with SHSC institutional
privacy and confidentiality policies
> Curate and preprocess datasets to create robust test sets for AI models
> Evaluate model performance using clinically acquired imaging data
> Collaborate closely with neuroradiologists and oncologists to review model outputs
> Integrate clinical feedback through iterative review and validation with
clinicians
> Aimed at ensuring safe, clinically relevant integration of AI tools in healthcare,
supporting faster decision-making and reducing the workload on clinicians
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, Nauman Bashir, and Ali Sadeghi-Naini. "Track, Measure, Evaluate: A Clinically Aligned Pipeline for Automatic Radiotherapy Response Assessment in Brain Tumors." 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI). IEEE, 2026.
Bhatti, Nauman Bashir, et al. "Longitudinal assessment of radiosurgery response in small brain metastases: AI-driven precision tumor segmentation and monitoring on serial MRI" Medical Physics (2026).
Bhatti, Nauman Bashir, et al. "Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis." International Journal of Radiation Oncology* Biology* Physics (2025).
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.
Artificial Intelligence, Python, deep learning, and applied machine learning.
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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