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Ghulam Rasool
Ghulam Rasool
Machine Learning Department, Moffitt Cancer Center
Verified email at moffitt.org - Homepage
Title
Cited by
Cited by
Year
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
IE Nielsen, D Dera, G Rasool, N Bouaynaya, RP Ramachandran
IEEE Signal Processing Magzine 39 (4), 73-84, 2022
862022
Transformers in Time-Series Analysis: A Tutorial
S Ahmed, IE Nielsen, A Tripathi, S Siddiqui, RP Ramachandran, G Rasool
Circuits, Systems, and Signal Processing, 1-34, 2023
722023
Real-time task discrimination for myoelectric control employing task-specific muscle synergies
G Rasool, K Iqbal, N Bouaynaya, G White
IEEE transactions on neural systems and rehabilitation engineering 24 (1 …, 2015
652015
Inception Modules Enhance Brain Tumor Segmentation
DE Cahall, G Rasool, NC Bouaynaya, HM Fathallah-Shaykh
Frontiers in computational neuroscience 13, 44, 2019
572019
Spatial analysis of multichannel surface EMG in hemiplegic stroke
G Rasool, B Afsharipour, NL Suresh, WZ Rymer
IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (10 …, 2017
442017
Constrained State Estimation--A Review
N Amor, G Rasool, NC Bouaynaya
arXiv preprint arXiv:1807.03463, 2018
36*2018
PremiUm-CNN: Propagating Uncertainty Towards Robust Convolutional Neural Networks
D Dera, N Bouaynaya, G Rasool, R Shterenberg, H Fathallah-Shaykh
IEEE Transactions on Signal Processing, 2021
272021
Extended Variational Inference for Propagating Uncertainty in Convolutional Neural Networks
D Dera, G Rasool, N Bouaynaya
IEEE International Workshop on Machine Learning for Signals Processing (MLSP), 2019
272019
Shear Waves Reveal Viscoelastic Changes in Skeletal Muscles after Hemispheric Stroke
G Rasool, AB Wang, WZ Rymer, SSM Lee
IEEE Transactions on Neural Systems and Rehabilitation Engineering 26 (10), 1-9, 2018
182018
Altered Viscoelastic Properties of Stroke-Affected Muscles Estimated Using Ultrasound Shear Waves—Preliminary Data
G Rasool, AB Wang, WZ Rymer, SSM Lee
2016 38th Annual International Conference of the IEEE Engineering in …, 2016
182016
Bayes-SAR Net: Robust SAR image classification with uncertainty estimation using Bayesian convolutional neural network
D Dera, G Rasool, NC Bouaynaya, A Eichen, S Shanko, J Cammerata, ...
2020 IEEE International Radar Conference (RADAR), 362-367, 2020
172020
Scopeformer: n-CNN-ViT hybrid model for Intracranial hemorrhage subtypes classification
Y Barhoumi, G Rasool
Medical Imaging with Deep Learning, 2021
16*2021
Neural drive estimation using the hypothesis of muscle synergies and the state-constrained Kalman filter
G Rasool, K Iqbal, N Bouaynaya, G White
2013 6th international IEEE/EMBS conference on neural engineering (NER), 802-805, 2013
162013
Muscle activity detection from myoelectric signals based on the AR-GARCH model
G Rasool, N Bouaynaya, K Iqbal
2012 IEEE Statistical Signal Processing Workshop (SSP), 420-423, 2012
162012
Vision transformer and language model based radiology report generation
MM Mohsan, MU Akram, G Rasool, NS Alghamdi, MAA Baqai, M Abbas
IEEE Access 11, 1814-1824, 2022
152022
Exploring robust architectures for deep artificial neural networks
A Waqas, H Farooq, NC Bouaynaya, G Rasool
Communications Engineering 1 (1), 46, 2022
14*2022
Surface myoelectric signal classification using the AR-GARCH model
G Rasool, N Bouaynaya, K Iqbal, G White
Biomedical Signal Processing and Control 13, 327-336, 2014
142014
Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models
A Waqas, MM Bui, EF Glassy, I El Naqa, P Borkowski, AA Borkowski, ...
Laboratory Investigation, 100255, 2023
132023
Dilated inception U-net (DIU-net) for brain tumor segmentation
DE Cahall, G Rasool, NC Bouaynaya, HM Fathallah-Shaykh
arXiv preprint arXiv:2108.06772, 2021
132021
Using surface electromyography to detect changes in innervation zones pattern after human cervical spinal cord injury
B Afsharipour, MS Sandhu, G Rasool, NL Suresh, WZ Rymer
2016 38th Annual International Conference of the IEEE Engineering in …, 2016
132016
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