Sheethal Bhat

Education: 

• Since 03/2023:
  Ph.D. Researcher at Pattern Recognition Lab and Siemens Healthineers
• 10/2020-12/2022:
  Student at Friedrich-Alexander-Universität Erlangen-Nürnberg, Information and Communication Theory (ICT)
  Masters thesis : Normals vs Abnormal Chest X-Ray classification using Self-supervised Contrastive Learning.
• 01/2007-06/2008:
  Student at Carnegie Mellon University, Pittsburgh, USA, Image processing and Pattern recognition

Professional Employment:

• 2004-2006:
  IBM Software Services, Bangalore, India
• 2008-2014:
  Low power imaging Power and Performance systems Architect, Intel, USA

2023

• Self-Supervised Learning on Chest X-Rays to improve classification and localization

  (Non-FAU Project)

  Term: March 1, 2023 - March 1, 2026
  Abstract

  Chest X-Rays (CXR) serve as crucial diagnostic tools for pulmonary and cardiothoracic diseases, generating millions of images daily, a number on the rise due to decreasing acquisition costs. However, there's a pronounced scarcity of radiologists to interpret these images. Traditionally, CXR research has centered on enhancing classification accuracy, often achieving state-of-the-art results. Despite progress, there remain rare and intricate findings challenging for both human radiologists and AI systems to diagnose. Our investigation focuses on leveraging self-supervised image-text models to enhance the classification and localization of diverse findings. These self-supervised models eliminate the need for annotations, enabling the Deep Learning system to effectively learn from extensive public and private datasets.

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