Sheethal Bhat

Researcher

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

Room: Room 09.158
Martensstraße 3
91058 Erlangen

8:00 - 11:00

Please contact via email

 

Education: 

  • 03/2023-2026:
    Ph.D. Researcher at Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg and Siemens Healthineers, Erlangen
  • 10/2020-12/2022:
    MSc. Information and Communication Theory (ICT), Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
    Thesis : Normals vs Abnormal Chest X-Ray Classification Using Self-supervised Contrastive Learning
  • 01/2007-06/2008:
    MSc. Image Processing and Pattern Recognition, Carnegie Mellon University, Pittsburgh, USA
  • 08/2000-06/2004:
    BTech. Electronics and Communications Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India

Professional Employment:

  • 2021-2022:
    Student HiWi/Research Assistant, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
  • 2008-2014:
    Low power Imaging Power and Performance Systems Architect, Intel Corp., Portland, Oregon, USA
  • 2007-2008:
    Student Research Assistant, Carnegie Mellon University, Pittsburgh, USA
  • 2004-2006:
    Software developer, IBM Software Services, Bangalore, India

2023

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

    (Non-FAU Project)

    Project leader:
    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.

2026

Conference Contributions

Unpublished Publications

2025

Journal Articles

Conference Contributions

Miscellaneous

2024

Conference Contributions

2023

Journal Articles

2008

Conference Contributions

2023

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

2013

  • : Divisional Recognition Award (Intel Inc. USA) – 2013

2012

  • : Divisional Recognition Award (Intel Inc. USA) – 2012

2009

  • : Spontaneous Recognition Award (Intel Inc. USA) – 2009

2006

No matching records found.

 

Granted

  • Image-text deep neural network algorithm for patch-wise prediction of pathology findings,
    Sheethal Bhat, Bogdan Georgescu, Awais Mansoor, Florin-C Ghesu, Sasa Grbic, US Patent App. 18/985,120, (2025)
  • Method, apparatus, system, and computer readable medium for image processing software module configuration,
    Sheethal Bhat, Madhu Athreya, YLE Chang, US Patent 9,800,781, (2017)
  • Image capture Sensor configurations,
    R Chukka, R Poornachandran, S Dadu, AK Mishra, Sheethal Bhat, US Patent 9,723,212, (2017)
  • System, method, and apparatus for an edgee-preserving smooth filter for low power architecture,
    J Zhou, O Nestares, Sheethal Bhat, Madhu Athreya, US Patent 8,471,865, (2013)

Filed

  • Confidential – 2025

Title Type Status Student
Coordination Under Uncertainty in Multi-Team ADAS Testing: How Multi-Agent AI Systems Shape Transactive Memory Systems MA thesis running Priyadharshan Velmurugan
Evaluate few-shot detection on VinDR-CXR Project finished Mahdi Qanbari
RAG-Enhanced Low-Cost Vision-Language Models for Diabetic Retinopathy Classification and Automated Reporting MA thesis running Abdelrahman Abdelmonem Abdelrazek Zaian
Evaluate LORA finetuning for detection guided segmentation in CT images MA thesis running Robaitul Islam
Improving localization of VL models in CXRs MA thesis running Harsha
Leveraging data for improved contrastive loss in CXR classification MA thesis running Florian Kittler
Scene detection and automatic scene description – External MA thesis running Abdul Dost
3D detection of Abdominal Trauma in CT images using cross-attention Project running
Dynamic Gap Closure Forecasting in the DAX Index MA thesis finished Samarth Khajuria
Unsupervised detection Project finished Daniel Rosenberger
Improved Zero-shot Pathology detection in medical images MA thesis running Atharv
Enhancing Breast Abnormality Detection on Mammograms with Advanced Vision-Language Models MA thesis finished Karim Mohamed Khalifa Abouzeid Elbarbary
Evaluation of stenosis detection in angiography images Project finished Muhammad Musab Ansari
Comparative Evaluation of Deep Learning Models for Chest X-ray Lesion Detection Project finished Shubham Gupta
Evaluate Simpleshot: Simple implementation of few-shot classification on CXRs Project finished Samarth Khajuria
Deep-learning based long-tailed multi-label chest X-ray disease classification MA thesis finished Rajesh Madhipati
Evaluation of detection performance on CXR dataset using DETR pipeline Project finished Anshul Dhingra
Evaluation of few-shot localization in Chest X-Rays (CXRs) Project open
Improving Few Shot Classification in Chest X-rays MA thesis finished Hamza Naeem
Review of Zero-shot, Few-shot classification, detection and segmentation methods in Medical Imaging Project finished Abushanab, Mohammad Yahia, Maged Badawi
Evaluation of MedKLIP for Zero-shot and Fine-tuned classification of CXRs Project finished Sushmetha Arumugam
Improvements in SSL image-text learnings on CXR images MA thesis finished Prakhar Bharadwaj
AI-Driven Monuments Identification System and its details Project finished Hamza Naeem