Navchetan Awasthi

Employment

• Eindhoven University of Technology Postdoc
  2020 - Present
• Massachusetts General Hospital, Harvard University Research Fellow
  2019 - 2020

Education

• Indian Institute of Science Ph. D.
  2016 - 2018
• Indian Institute of Science M. Tech.
  2014 - 2016
• Dr BR Ambedkar National Institute of Technology B. Tech.
  2007 - 2011

Projects & Funding

Projects & funding information is unavailable.

Publications (16)

• Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19
  IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2021 DOI: 10.1109/TUFFC.2021.3068190
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• Low-Cost, Continuous Motion Imaging, Computationally Augmented Whole Slide Imager for Digital Pathology
  IEEE Journal of Selected Topics in Quantum Electronics 2021 DOI: 10.1109/jstqe.2021.3067389
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• Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals
  Telemedicine and e-Health 2021 DOI: 10.1089/tmj.2020.0507
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• Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data
  Biomedical Optics Express 2021 DOI: 10.1364/BOE.415182
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• Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans
  arXiv 2021 DOI: 10.48550/arXiv.2101.12404
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• State-of-the-art equipment for rapid and accurate diagnosis of COVID-19
  Biomedical Engineering Tools for Management for Patients with COVID-19 2021
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• Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans
  2021 DOI: 10.1007/978-3-030-72087-2_15
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• State-of-the-art equipment for rapid and accurate diagnosis of COVID-19
  Biomedical Engineering Tools for Management for Patients with COVID-19 2021 DOI: 10.1016/b978-0-12-824473-9.00012-4
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• Classification of 12 Lead ECG Signal Using 1D-CNN With Class Dependent Threshold
  2020 Computing in Cardiology Conference (CinC) 2020 DOI: 10.22489/cinc.2020.277
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• Machine Learning and Deep Learning Approaches to Quantify Respiratory Distress Severity and Predict Critical Alarms
  2020 IEEE International Conference on Healthcare Informatics (ICHI) 2020 DOI: 10.1109/ichi48887.2020.9374301
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