Nahid Ul Islam About CV Research Projects Activities

Welcome!

Nahid

Nahid Ul Islam
Ph.D. Candidate in Computer Science
Arizona State University

Summary

I am a Ph.D. candidate in Computer Science at Arizona State University, specializing in Computer Vision, Deep Learning, and Medical Image Analysis. My research focuses on developing annotation-efficient and generalizable deep learning frameworks, with a strong record of publications in leading journals and conferences. I also hold multiple granted and pending patents, reflecting my commitment to advancing state-of-the-art AI solutions for real-world applications.

Beyond academia, I completed a six-month internship at Intel Corporation, where I applied deep learning and computer vision techniques to challenging industry problems. I bring extensive programming expertise in Python, MATLAB, Java, and C/C++, with a solid foundation in algorithms, data structures, and database design. My background also includes strong experience in object-oriented analysis, design, and implementation using modern C++ (STL) and Java.

Education

Ph.D. in Computer Science Arizona State University, USA (2018 – Present)
Supervisor: Dr. Jianming Liang
Research Area: Computer Vision, Deep Learning, Medical Image Analysis
M.Sc. in Computer Science University of Texas at San Antonio, USA (2015 – 2017)
Supervisor: Dr. Qi Tian
Research Area: Computer Vision, Machine Learning, Image Processing
B.Sc. in Computer Science BRAC University, Bangladesh (2010 – 2014)
Supervisor: Rubel Biswas
Research Area: Image Processing

Experience

Graduate Research Assistant Arizona State University, Tempe, AZ (Aug 2018 – Present)
– Published five conference papers and two journal papers in Computer Vision and Medical Image Analysis.
– Hold two granted and five pending patents, demonstrating strong research contributions.
Graduate Technical Intern Intel Corporation, Client Computing Group, Hillsboro, OR (May 2017 – Aug 2017)
– Introduced deep learning and computer vision approach for obstacle detection/classification and collision prediction for moving objects using different deep neural network implementations (i.e. R-CNN, Fast RCNN, Faster RCNN, Mask RCNN).
– Investigated and analyzed the results from the experiments and presented findings as well as data to the team towards pathfinding/technical readiness.
Graduate Software Engineering Intern Intel Corporation, Client Computing Group, Hillsboro, OR (Feb 2017 – May 2017)
– Researched the application of Deep Learning technologies to recognize Human Activities from video, with application to novel peace-of-mind Smart Home usages.
– Developed a human activity recognition system based on computer vision and deep learning technology, starting with an established deep learning network framework, and adapting it to the project requirements by configuring metadata, customizing scripts, iteratively making changes, and checking results to improve the accuracy/results etc. Used different deep learning and computer vision frameworks/algorithms such as CAFFE, convolutional neural network, optical flow.
– Enhanced the direction of the different phases of the project by thoroughly analyzing the data collected by the iterative experiments. Moreover, data analysis was done for evaluating results of multiple methods and comparison between them.

Thesis

Ph.D. Thesis Arizona State University
Title: Towards Generalizable Annotation-Efficient Medical Image Analysis via Classification, Localization, and Segmentation Supervision
– Research on developing foundation models for medical image analysis via large-scale pretraining across multiple datasets and tasks. Leveraging diverse annotations spanning classification, localization, and segmentation within a single end-to-end framework, while designing strategies that ensure synergy across tasks. Aiming to build annotation-efficient frameworks that enable scalable and generalizable models, lower annotation costs, and improve clinical reliability.
M.Sc. Thesis University of Texas at San Antonio
Title: Human Face Detection Followed by Gender and Age Estimation Using Patch-Based DCT and Histogram of Oriented Gradients
– Worked on Face Detection, Gender Classification and Age Estimation classification algorithm enhancement using Discrete Cosine Transformation, Histogram of Oriented Features as Feature Extraction and Convolutional Neural Network and Support Vector Machine as Machine Learning techniques.
B.Sc. Thesis BRAC University
Title: Automated Parking Lot Management for Bengali License Plates Using Hough Transformation and Image Segmentation
– Worked on Automated parking lot management system for Bengali Language, by using Connected Component labeling, Bounding Box, Canny Edge Detection & Hough Transformation for license plate localization and used OCR for pattern recognition following by database to store as well as manipulating the system.

Peer-refereed Journal Publications

2024 1. Islam, N. U., Zhou, Z., Gehlot, S., Gotway, M. B., & Liang, J. Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism. Medical Image Analysis, 91, 102988.
2024 2. Guo, Z., Islam, N. U., Gotway, M. B., & Liang, J. Stepwise incremental pretraining for integrating discriminative, restorative, and adversarial learning. Medical Image Analysis, 103159.

Peer-refereed Conference Full Publications

2025 1. Islam, N. U., Ma, D., Pang, J., Senthil Velan, S., Gotway, M. B., & Liang, J. Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis. WACV.
2022 2. Guo, Z., Islam, N. U., Gotway, M. B., & Liang, J. Discriminative, restorative, and adversarial learning: Stepwise incremental pretraining. MICCAI Workshop on Domain Adaptation and Representation Transfer.
2022 3. Pang, J., Haghighi, F., Ma, D., Islam, N. U., Hosseinzadeh Taher, M. R., Gotway, M. B., & Liang, J. POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis. MICCAI Workshop.
2022 4. Ma, D., Hosseinzadeh Taher, M. R., Pang, J., Islam, N. U., Haghighi, F., Gotway, M. B., & Liang, J. Benchmarking and boosting transformers for medical image classification. MICCAI Workshop.
2021 5. Islam, N. U., Gehlot, S., Zhou, Z., Gotway, M. B., & Liang, J. Seeking an optimal approach for computer-aided pulmonary embolism detection. MLMI 2021 (MICCAI Workshop).

Publicly Released Software

1. Foundation X: An end-to-end model integrating classification, localization, and segmentation tasks [GitHub]
2. Stepwise Incremental Pretraining: Achieving discriminative, restorative, and adversarial learning [GitHub]
3. Benchmarking Transformers: Benchmarking and boosting transformers for medical image classification [GitHub]
4. POPAR: Restoring patch order and appearance for self-supervised medical image analysis [GitHub]
5. Evaluating and optimizing deep learning methods for computer-aided diagnosis of pulmonary embolism [GitHub]

Patents

Granted 1. Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou, Jianming Liang. U.S. Patent No. US12236592B2, “Systems, methods, and apparatuses for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism” issued Feb 25, 2025.
2. DongAo Ma, Jiaxuan Pang, Nahid Ul Islam, Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Jianming Liang. U.S. Patent No. US12394186B2, “Systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer for use with medical image classification” issued Aug 19, 2025.
Pending 1. Nahid Ul Islam, DongAo Ma, Jiaxuan Pang, Shivasakthi Senthil Velan, Jianming Liang. U.S. Provisional Patent Application No. 63/744,744, “Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis,” filed Jan 13, 2025 (patent pending).
2. Madhumitha Saravan, Nahid Ul Islam, Jiaxuan Pang, Jianming Liang. U.S. Provisional Patent Application No. 63/766,286, “Benchmarking and Boosting of 3D Segmentation Models” filed Mar 3, 2025 (patent pending).
3. Zuwei Guo, Nahid Ul Islam, Jianming Liang. U.S. Patent Application No. US20250029372A1, “Systems, methods, and apparatuses for implementing stepwise incremental pre-training for integrating discriminative, restorative, and adversarial learning into an AI model” filed July 17, 2024 (patent pending).
4. Zuwei Guo, Nahid Ul Islam, Jianming Liang. U.S. Patent Application No. US20240078434A1, “Systems, methods, and apparatuses for implementing discriminative, restorative, and adversarial (DiRA) learning using stepwise incremental pre-training for medical image analysis” filed Sept 1, 2023 (patent pending).
5. Jiaxuan Pang, Fatemeh Haghighi, DongAo Ma, Nahid Ul Islam, Mohammad Reza Hosseinzadeh Taher, Jianming Liang. U.S. Patent Application No. US20240078666A1, “Systems, methods, and apparatuses for implementing patch order prediction and appearance recovery (popar) based image processing for self-supervised learning medical image analysis” filed Sept 1, 2023 (patent pending).

Talks and Presentations

Technical Skills

Programming Language: C/C++, Java, MatLab, Python
Scripting Language: Shell, PHP, Html, XML, CSS, JavaScript
Frameworks/Libraries: Caffe, TensorFlow, TFLearn, Keras, OpenCV, Scikit-learn
Mathematical Tools: Matplotlib, NumPy
Database: MySQL, SQLite
App Design: Android Studio, Android SDK, Eclipse with ADT plugins
Operating System: Windows, Linux

Timeline

Dec, 24' Received the prestigious President’s Award for Innovation, Arizona State University — Project: “Annotation-efficient Deep Learning for Computer-aided Diagnosis in Medical Imaging.”
May, 24' Certificate of GPSA Travel Grant Reviewer, Arizona State University.
May, 23' Certificate of GPSA Travel Grant Reviewer, Arizona State University.
Aug, 22' Travel Grant, MICCAI 2022, awarded by the GPSA (Arizona State University).
Apr, 22' Travel Grant, CVPR 2022, awarded by the GPSA (Arizona State University).
Aug, 18' Started Ph.D in Computer Science at Arizona State University.
May, 18' Received prestigious CIDSE Doctoral Fellowship from the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University.
Mar, 18' Got admission into Ph.D program in Computer Science at Arizona State University.
Dec, 17' Completed M.Sc. in Computer Science from University of Texas at San Antonio, USA.
Nov, 17' Sucessfully defended Masters Thesis at University of Texas at San Antonio.
May, 17' Joined Intel Corporation for the summer'17 semester as Graduate Technical Intern.
Feb, 17' Joined Intel Corporation for the spring'17 semester as Graduate Software Engineering Intern.
Aug, 15' Started M.Sc. in Computer Science at University of Texas at San Antonio, USA.
Nov, 14' Completed B.Sc. in Computer Science from BRAC University, Bangladesh.