Ghulam Rasool, PhD
![Ghulam Rasool](/globalassets/images/researchers/RasoolGhulam_32214.jpg)
Ghulam Rasool, PhD
Program: Machine Learning
Research Program: Cancer Epidemiology Program
-
Overview
Dr. Rasool's research is focused on building robust, trustworthy, safe, and fair AI for improving cancer care. His lab is developing deep and machine learning models for integrating multi-modal, multi-resolution, heterogeneous datasets to answer clinically relevant questions and optimize treatment decisions for personalized cancer care.
Associations
- Neuro-Oncology
- Machine Learning
- Cancer Epidemiology Program
Education & Training
Graduate:
- University of Arkansas at Little Rock, PhD - Systems Engineering
- Centre for Advanced Studies in Engineering, MSc - Computer Engineering
Fellowship:
- Shirley Ryan AbilityLab -
-
Research Interest
Dr. Rasool's research is focused on building robust, trustworthy, safe, and fair AI for improving cancer care. His lab is developing deep and machine learning models for integrating multi-modal, multi-resolution, heterogeneous datasets to answer clinically relevant questions and optimize treatment decisions for personalized cancer care.
-
Participating Trials
Clinical Trial 23105
Evaluating the Safety, Reliability, and Utility of AI Chatbots in Providing Cancer-Related Information
Condition: Multiple
Status: OpenIf you believe you are eligible for one of these trials or studies, please call
813-745-6100 or toll-free 1-800-679-0775. -
Publications
- Tripathi A, Waqas A, Venkatesan K, Yilmaz Y, Rasool G. Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets. Sensors (Basel). 2024 Mar.24(5). Pubmedid: 38475170. Pmcid: PMC10933897.
- Park MA, Whelan CJ, Ahmed S, Boeringer T, Brown J, Crowder SL, Gage K, Gregg C, Jeong DK, Jim HSL, Judge AR, Mason TM, Parker N, Pillai S, Qayyum A, Rajasekhara S, Rasool G, Tinsley SM, Schabath MB, Stewart P, West J, McDonald P, Permuth JB. Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration. Cancers (Basel). 2024 Jun.16(13). Pubmedid: 39001427. Pmcid: PMC11240731.
- Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel). 2024 Jun.16(12). Pubmedid: 38927945. Pmcid: PMC11201559.
- Permuth JB, Park MA, Chen DT, Basinski T, Powers BD, Gwede CK, Dezsi KB, Gomez M, Vyas SL, Biachi T, Cortizas EM, Crowder S, Genilo-Delgado M, Green BL, Greene A, Gregg C, Hoffe SE, Jiang K, Kim B, Vasudevan V, Garcialopez De Llano J, Menon AA, Mo Q, MorenoUrazan LM, Mok S, Parker N, Rajasekhara S, Rasool G, Sinnamon A, Sparks L, Stewart PA, Tardif K, Tassielli AF, Teer JK, Tran DV, Turner KL, Vadaparampil ST, Whelan CJ, Douglas WG, Velanovich V, Karachristos A, Legaspi A, Meredith K, Molina-Vega MA, Huguet KL, Arnoletti JP, Bloomston M, Trevino J, Merchant NB, Pimiento JM, Hodul PJ, Malafa M, Fleming J, Judge SM, Jeong DK, Judge A. Leveraging real-world data to predict cancer cachexia stage, quality of life, and survival in a racially and ethnically diverse multi-institutional cohort of treatment-naïve patients with pancreatic ductal adenocarcinoma. Front Oncol. 2024 Jul.14:1362244. Pubmedid: 39109281. Pmcid: PMC11300308.
- Waqas A, Tripathi A, Ramachandran RP, Stewart PA, Rasool G. Multimodal data integration for oncology in the era of deep neural networks: a review. Front Artif Intell. 2024 Jul.7:1408843. Pubmedid: 39118787. Pmcid: PMC11308435.
- Waqas A, Tripathi A, Mukund A, Stewart P, Naeini M, Rasool G. BIO24-031: Hierarchical Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes. J Natl Compr Canc Ne. 2024 Apr.22(2.5). Pubmedid: 38579762.
- Waqas A, Bui MM, Glassy EF, El Naqa I, Borkowski P, Borkowski AA, Rasool G. Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models. Lab Invest. 2023 Nov.103(11):100255. Pubmedid: 37757969.
- Epifano JR, Ramachandran RP, Masino AJ, Rasool G. Revisiting the fragility of influence functions. Neural Netw. 2023 May.162:581-588. Pubmedid: 37011460.
- Ahmed S, Dera D, Hassan SU, Bouaynaya N, Rasool G. Failure Detection in Deep Neural Networks for Medical Imaging. Front Med Technol. 2022 Jul.4:919046. Pubmedid: 35958121. Pmcid: PMC9359318.
- Cahall DE, Rasool G, Bouaynaya NC, Fathallah-Shaykh HM. Inception Modules Enhance Brain Tumor Segmentation. Front Comput Neurosci. 2019 Jul.13:44. Pubmedid: 31354462. Pmcid: PMC6640210.
- Rasool G, Wang AB, Rymer WZ, Lee SSM. Shear Waves Reveal Viscoelastic Changes in Skeletal Muscles After Hemispheric Stroke. IEEE Trans Neural Syst Rehabil Eng. 2018 Oct.26(10):2006-2014. Pubmedid: 30334740. Pmcid: PMC6471515.
- Rasool G, Afsharipour B, Suresh NL, Rymer WZ. Spatial Analysis of Multichannel Surface EMG in Hemiplegic Stroke. IEEE Trans Neural Syst Rehabil Eng. 2017 Oct.25(10):1802-1811. Pubmedid: 28320672. Pmcid: PMC6492268.
- Rasool G, Iqbal K, Bouaynaya N, White G. Real-Time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies. IEEE Trans Neural Syst Rehabil Eng. 2016 Jan.24(1):98-108. Pubmedid: 25769166.
- Rasool G, Afsharipour B, Suresh NL, Xiaogang Hu, Rymer WZ. Spatial analysis of muscular activations in stroke survivors. Annu Int Conf IEEE Eng Med Biol Soc. 2015 Dec.2015:6058-6061. Pubmedid: 26737673.
- Rasool G, Iqbal K. Muscle activity onset detection using energy detectors. Annu Int Conf IEEE Eng Med Biol Soc. 2012 Dec.2012:3094-3097. Pubmedid: 23366579.
-
Grants
- Title: Automated De-Identification of Pathology and Radiology Data
Sponsor: Nat Institutes of Health
PI: Rasool, G., CO-PI: Folio, L., CO-PI: Bui, M., CO-PI: Farinhas, J. - Title: I-Corps Teams: Detecting Performance Degradation and Failures of Deep Neural Networks in Cancer Imaging
Sponsor: Nat Science Foundation
PI: Rasool, G. - Title: SCenE - Self-Assessment and Continual Learning on Edge Devices
Sponsor: Nat Science Foundation
PI: Rasool, G. - Title: PFI-TT: Trustworthy Artificial Intelligence for the Volumetric Evaluation of Brain Tumors - Robust Learning for Medical Image Segmentation
Sponsor: Nat Science Foundation
PI: Rasool, G., CO-PI: Rieger, J.
- Title: Automated De-Identification of Pathology and Radiology Data