Summary
Dr. Shahab Ansari (HEC Approved PhD Supervisor) acquired Bachelor’s degree in Electronics from NED University, Pakistan, in 1987. Dr. Shahab Ansari earned his Master’s degree in speech enhancement in hearing aids from McMaster University, Canada, in 2005. He completed his PhD in 2017 in numerically solving parabolic partial differential equations using stabilized mixed Galerkin method from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Pakistan. Currently, he is supervising various projects in Artificial Intelligence in Medicine (AIM) Lab and teaching undergraduate and graduate courses in the Faculty of Computer Science and Engineering in GIK institute.
AIM Lab
Artificial Intelligence in Medicine (AIM) lab has been involved in quality research work in medical diagnostics and treatment using medical imaging and Artificial Intelligence (AI) since 2010. The lab has won a number of grants from Directorate of Science and Technology (DoST), KPK, and HEC, Pakistan. A grant of amount PKR480,000 from DoST was acquired for the implementation of automatic classification of prostate cancer tissues using wavelet packet transformation using histological images. Another project, on automatic segmentation of subcortical regions using nonlinear warping technique on brain MRIs was published in a FIT conference in 2010. In 2017, the lab also received a HEC grant of PKR452,300 for brain image analysis with tumors using AI techniques. Recently, a HEC-NRPU grant of PKR 1.1 million has been acquired for the characterization of Alzheimer’s disease in MRI images using deep neural networks. The lab has also been involved in various digital image processing based and hardware-based final year projects for undergraduate students.
AIM Lab: https://sites.google.com/view/aimlab/home
Journal Publications & Conferences
Ahmad, S., Ansari, S.U., Haider, U. et al. Confusion matrix-based modularity induction into pretrained CNN. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-12331-2
Waqas Ahmed, Shahab Ansari, Muhammad Hanif, and Akhtar Khalil. PCA driven mixed filter pruning for efficient convNets. Plos One, 2022. https://doi.org/10.1371/journal.pone.0262386
Ullah, Faizad, Shahab U. Ansari, Muhammad Hanif, Mohamed A. Ayari, Muhammad E.H. Chowdhury, Amith A. Khandakar, and Muhammad S. Khan 2021. “Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net” Sensors21, no. 22: 7528. https://doi.org/10.3390/s21227528
Fatima Ali, Babar Hassan, Huzaifa Ahmad, Zahra Hoodbhoy, Zainab Bhuriwala, Muhammad Hanif, S. U Ansari, Devyani Chodhury, “Detection of Subclinical Rheumatic Heart Disease in Children using a Deep Learning Algorithm on Digital Stethoscope: A Study Protocol,” BMJ 2021.
S. U. Ansari, Kamran Javed, Saeed Mian Qiasar, Rashid Jilani and Usman Haider, “Multiple Sclerosis Lesion Segmentation in Brain MRI using Inception Modules Embedded in Convolutional Neural Networks,” Journal of Healthcare Engineering, 2021
N. Ali, S. U. Ansari, Z. Halim, R. H. Ali, M. F. Khan and M. Khan, “Breast Cancer Classification and Proof of Key Aritificial Neural Network Terminologies”, 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2019.
M. Hussain, S. U. Ansari, T. Manzoor, A. Ahmad, K. I. Khan, “Performance Analysis of Parallel Stabilized Mixed Galerkin Method for Three-Dimensional Transient Darcy Flow using Mesh Reordering Techniques,” Journal of Petroleum Science and Engineering, 2019
S. U. Ansari, M. Hussain, A. Rashid, S. Mazhar, S. M. Ahmad, “Numerical Solution and Analysis of Three-Dimensional Transient Darcy Flow,” Transport in Porous Media, 20-March, 2018. DOI:10.1007/s11242-018-1041-2.
S. U. Ansari, M. Hussain, A. Rashid, S. M. Ahmad, S. Mazhar, and K. J. Siddiqui, “Validating Numerical Solution of Transient Darcy Flow using Stabilized Mixed Finite Element Method,” Simulation: Transactions of the Society for Modeling and Simulation International, 2017.
S. U. Ansari, M. Hussain, S. Mazhar, T. Manzoor, K. J. Siddiqui, M. Abid and H. Jamal, “Mesh Partitioning and Efficient Equation Solving Techniques by Distributed Finite Element Methods: A Survey,” Archives of Computational Methods in Engineering, 2017.
S. U. Ansari, M. Hussain, S. M. Ahmad, A. Rashid and S. Mazhar, “Stabilized Mixed Finite Element Method for Transient Darcy Flow,” Transactions of the Canadian Society for Mechanical Engineering, 41(1):85-97, 2017.
S. U. Ansari, M. Hussain, S. Mazhar, A. Rashid and S. M. Ahmad, “Parallel Stabilized Mixed Galerkin Method for Three-Dimensional Darcy Flow,” NSEC, Islamabad, 2015.
S. U. Ansari, M. Hussain, S. Mazhar, A. Rashid and S. M. Ahmad, “Three-Dimensional Stabilized Mixed Galerkin Method for Darcy Flow,” FIT, Islamabad, July, 2015.
S. U. Ansari, M. Hussain, S. Mazhar, A. Rashid and S. M. Ahmad, “Stabilized Mixed Galerkin Method for Transient Analysis of Darcy Flow,” ICMSAO, Istanbul, Turkey, May, 2015.
F. Waqar, H. Qureshi, M. Hussain, S. U. Ansari, “Texture Classification using Discriminant Wavelet Packet Sub-bands and Support Vector Machines”, WEC, 2013.
S. U. Ansari and S. Mansha. “Simulation-Based Hardness Evaluation of a Multi-Objective Genetic Algorithm.” ICOMS, Islamabad, 2013.
M. Abid, A. Khan, S. U. Ansari. Selection of optimum girders (rolled section) for overhead cranes using finite element analysis. 9th International Conference on Fracture & Strength of Solids, Jeju, Korea. Pp. 1-5, 2013.
S. U. Ansari, “Validation of FS+LDDMM by automatic segmentation of caudate nucleus in brain MRI” Frontier of Information Technology (FIT), 2010.
S. U. Ansari and M. F. Beg, “Template-based brain MRI registration/segmentation using LDDMM with a priori spatial knowledge” Human Brain Mapping, 2007.
S. U. Ansari and M. F. Beg, “Template-based brain MRI segmentation using LDDMM” Neuroscience Extravaganza, 2006.
N. Harte, S. U. Ansari and I. Bruce, “Exploiting voicing cues for contrast enhanced frequency shaping of speech for impaired listeners,” in Proceedings of 31st IEEE ICASSP, 2006.
S. U. Ansari, N. Harte and I. Bruce, “Efficiently combining improved contrast-enhancing frequency shaping and multiband compression to enhance speech intelligibility in hearing aids,” LOAN Meeting, 2005.
S. U. Ansari, H. Bajaj, K. Mustafa and I. Bruce, “Time efficient contrast-enhancing frequency shaping and multiband compression in hearing aids,” Abstracts of the IHCON, 2004.
I. Bruce, S. U. Ansari, H. Bajaj and K. Mustafa, “Multiband compression and contrast-enhancing frequency shaping in hearing aids,” in Proceedings of the 2004 Annual Conference of the Canadian Acoustical Association, 2004.
Graduate Student Supervision
- Mr. Faizad Ullah, Master’s Student, Tumor Segmentation in Brain MRI using Convolution Neural Networks.
- Mr. Ahmad Raza, Master’s Student, Characterization of Alzheimer’s Disease using Convolutional Neural Networks
- Mr. Muhammad Waqas, Master’s Student, Brain MRI Segmentation for the Identification and Classification of Schizophernia
- Mr. Najam Ur Rahman, Master’s Student, Brain MRI segmentation for the classification of Multiple Sclerosis using Machine Learning Techniques
- Mr. Hafiz Owais, Master’s Student, Shape analysis of subcortical regions in MRI using weighted spherical harmonics in ADHD data
- Ms. Reeda Saeed, Master’s Student, Deep neural networks for the analysis of MS lesions in brain MRI
Graduate Student Co-Supervision
- Mr. Salman Mehboob, Master’s Student, GIK Institute, Video Surveilance and Tracking. (Graduated in 2018)
- Mr. Fahad Waqar, Master’s Student, GIK institute, Automation of Gleason Grading using Wavelet Packet Transform. (Graduated & Published in WEC, 2013)
- Mr. Usman Ali, Master’s Student, Automatic Cancerous Tissue Classification. (Graduated in 2014)
- MS Sidra, PhD Scholar (UET Peshawar), Automatic Liver Tumor Segmentation using Deep Learning Techniques. (REC Committee)
- Mr. Salman Ahmad, Master’s Student, GIK Institute, Modular Convolutoinal Neural Network for image classification.
Research Funding
1. Pilot Research Studies, DoST, KPK (PI) – Automatic Gleason Grading in Prostate Cancer in Histological Images using SVM on Wavelet Features. PKR 0.45 Million
2. SRGP-HEC (PI) – Deep Learning-Based Tumor Detection and Classification in Brain MRI, PKR 0.48 Million
3. NRPU-HEC (PI) – Characterization of Alzheimer’s Disease using Convlutional Neural Network in MRI, PKR 1.15 Million
4. PSF-Tubitak (Co-PI) – Urinalysis of Microscopic Images using Deep Neural Networks, PKR 50 Million
5. Bill & Melinda Gates – CNN-based detection of rheumatic heart disease using digital stethoscope.
