Sheethal Bhat

Sheethal Bhat

Researcher

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

Room: Room 09.158
Martensstraße 3
91058 Erlangen

Office hours

 

Academic CV

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

Projects

2023

  • 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.

Publications

2025

Unpublished Publications

2024

Conference Contributions

2023

Journal Articles

Awards

2023

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

Lectures

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