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Ghulam  Rasool

Ghulam Rasool, PhD

Program: Machine Learning

Research Program: Cancer Epidemiology Program

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  • 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.

  • 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.
    • 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: 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: 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: 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.

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