Bio


Praveen joined the Nuclear Medicine and Molecular Imaging, Department of Radiology as a Postdoctoral Research Fellow where he will be working on predictive and real-time PET image quality monitoring using Machine learning. Previously, he worked as a Postdoctoral Research Associate at the University of Manchester, UK where he developed an inexpensive imaging & automated analysis system to support early diagnosis of Systemic Sclerosis. He received his Ph.D. in Electrical & Electronics Engineering from the Birla Institute of Technology and Science (BITS), Pilani. His Ph.D. research was focused on developing an automated intelligent decision support system for multiple brain disorder diagnosis from MRI scans. His research is mainly in the interdisciplinary field of deep learning and medical image analysis. In particular the application of deep learning models to improve disease prognosis. He completed his Master's in Biomedical Signal Processing & Instrumentation from RV College of Engineering, India. During his master's he worked as a project intern in the Department of Aerospace Engineering at the Indian Institute of Science (IISc, Bangalore) where he worked on the application of 3D digital image correlation to perform strain analysis on the human body during various physical activities.

Professional Education


  • Doctor of Philosophy, BITS Pilani - India, Electrical and Electronics Engineering (2019)
  • Master of Technology, R.V College of Engineering - India, Biomedical Signal Processing & Instrumentation (2011)
  • Bachelor of Engineering, NMAMIT - India, Computer Science Engineering (2009)

Stanford Advisors


Research Interests


  • Brain and Learning Sciences
  • Data Sciences

All Publications


  • Nailfold capillaroscopy: a survey of current UK practice and 'next steps' to increase uptake among rheumatologists RHEUMATOLOGY Eden, M., Wilkinson, S., Murray, A., Bharathi, P., Vail, A., Taylor, C. J., Payne, K., Herrick, A. L. 2022
  • OA08 Development of an automated deep learning-based system for distinguishing between ‘systemic sclerosis' and ‘normal'capillaries Gurunath Bharathi, P., Berks, M., Herrick, A., Dinsdale, G., Murray, A., Manning, J., Wilkinson, S., Taylor, C. 2022
  • P117 Nailfold capillaroscopy: a survey of current UK practice and ‘next steps’ to facilitate generalised uptake Eden, M., Wilkinson, S., Murray, A., Gurunath Bharathi, P., Taylor, C., Payne, K., Herrick, A. L. 2022
  • Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images BIOCYBERNETICS AND BIOMEDICAL ENGINEERING Bharathi, P., Agrawal, A., Sundaram, P., Sardesai, S. 2019; 39 (2): 410-425
  • Brain abnormality detection using template matching BIO-ALGORITHMS AND MED-SYSTEMS Praveen, G. B., Agrawal, A., Pareek, S., Prince, A. 2018; 14 (4)
  • Ischemic stroke lesion segmentation using stacked sparse autoencoder COMPUTERS IN BIOLOGY AND MEDICINE Praveen, G. B., Agrawal, A., Sundaram, P., Sardesai, S. 2018; 99: 38-52

    Abstract

    Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion region. Most existing methods mainly rely on designing hand-crafted features followed by a classifier model for ischemic stroke lesion segmentation. Design of these features requires complex domain knowledge and often lacks the ability to differentiate between the stroke lesions and the normal classes. In this work, we propose an unsupervised featured learning approach based on stacked sparse autoencoder (SSAE) framework for automatically learning the features for accurate segmentation of stroke lesions from brain MR images. A deep architecture is designed using sparse autoencoder (SAE) layers, followed by support vector machine (SVM) classifier for classifying the patches into normal or lesions. We validated our approach on a publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with a mean precision of 0.968, mean dice coefficient (DC) of 0.943, mean recall of 0.924 and mean accuracy of 0.904. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods in terms of precision, DC, and recall. Quantitative evaluation was carried out and compared with the existing approaches, which demonstrates that the proposed method is 25.71%, 36.67%, and 16.96% higher in terms of precision, DC and recall values, respectively. The unsupervised features learned via SSAE framework performs better than the hand-crafted features and can be easily trained on large datasets.

    View details for DOI 10.1016/j.compbiomed.2018.05.027

    View details for Web of Science ID 000442978700004

    View details for PubMedID 29883752

  • MediCloud: Cloud-Based Solution to Patient’s Medical Records Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 Praveen, G., Agrawal, A., Shah, J., Prince, A. Springer, Cham. 2018: 1099-1108
  • An analysis of leg muscle stretch using 3D digital image correlation International Journal of Organizational and Collective Intelligence (IJOCI) Praveen, G., Raghavendra, S., Chang, V. I. 2017; 7 (3)
  • Multi stage classification and segmentation of brain tumor Praveen, G. B., Agrawal, A., Hoda, M. N. IEEE. 2016: 1628-1632
  • Hybrid Approach for Brain Tumor Detection and Classification in Magnetic Resonance Images Praveen, G. B., Agrawal, A., Shukla, A., Chaturvedi, A., Bansal, A., Goyal IEEE. 2015: 162-166