Sheethal Bhat

Sheethal Bhat


Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 09.158
Martensstraße 3
91058 Erlangen

Office hours


Academic CV


  • 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



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

    (Non-FAU Project)

    Term: March 1, 2023 - March 1, 2026

    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.



Unpublished Publications


Conference Contributions


Journal Articles



  • : Best Masters thesis in ICT-Media systems (Siemens-Healthineers) – 2023


No matching records found.

Projects and Thesis

Type Title Status
MA thesis Improving Few-shot classification of CXR findings running
Project Review of Zero-shot, Few-shot classification, detection and segmentation methods in Medical Imaging finished
Project Evaluation of MedKLIP for Zero-shot and Fine-tuned classification of CXRs finished
MA thesis Improvements in SSL image-text learnings on CXR images running
Project AI-Driven Monuments Identification System and its details finished