Devesh Singh | University Medical Center (UMR), Rostock & German Center for Neurodegenerative Diseases (DZNE), Rostock

About Me

I’m a Ph.D. researcher at the University Medical Centre Rostock (UMR) and the German Center for Neurodegenerative Diseases (DZNE) in Rostock. Since April 2025, I have been supporting UMR in its implementation of the Open Medical Inference (OMI) platform to enable peer-to-peer exchange of healthcare data and access to remote AI services. Previously at DZNE, I was working within the Clinical Dementia Research Group under the guidance of Prof. Dr. Stefan Teipel and Dr. Martin Dyrba. In 2022, I graduated with an M.Sc. in Data and Knowledge Engineering from Otto von Guericke University (OvGU), Magdeburg, where I specialized in deep learning and computer vision.

My research focuses on building explainable AI models for disease detection using brain MRI data. I’m particularly interested in making deep learning systems more interpretable and clinically useful. One of my projects focused on developing self-supervised learning methods for analyzing brain scans and evaluating how well these models capture imaging features sensitive to neurodegenerative diseases. I’ve also worked on a semantic learning task where an ontology-based framework helped organize and visualize brain atrophy patterns, learning hierarchical rule-based features to make complex pathologic information more accessible. In the winter semester 2025/26, I co-supervised a Master’s thesis investigating a hybrid explainability approach combining rule-based reasoning with Large Language Models (LLMs) to generate clear, clinically relevant explanations from MRI-derived metrics.

Outside of academia, I have had the opportunity to serve as a Research Fellow at the Data Science for Social Good (DSSG) Fellowship, hosted by the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern in 2022. I have also worked with STIHL, SMA Solar Technology, and SICK in student roles, where I applied AI to real-world industrial problems. All these experiences continue to shape my passion for building AI tools that are not only technically robust but also practically impactful.

Research Interests

Computer Vision: Image classification, Semantic segmentation, Medical image analysis
Deep Learning: Self-supervised learning, Generative modeling, Explainable AI (XAI)

Publications

MIDL

Evidence-Supported Automated Impressions for Alzheimer’s Disease Detection from Brain MRI: A Feasibility Study

Devesh Singh, et al.

Medical Imaging with Deep Learning (MIDL)

DOI Code

Sci Rep

An Unsupervised XAI Framework for Dementia Detection with Context Enrichment

Devesh Singh et al.

Scientific Reports

DOI Code

CIBM

SMAS: Structural MRI-based AD Score using Bayesian supervised VAE

Aditya Nemali, Jose Bernal, Renat Yakupov, Devesh Singh, Martin Dyrba, et al.

Computers in Biology and Medicine

DOI

BVM

Evaluating the fidelity of explanations for convolutional neural networks in alzheimer’s disease detection

Bjarne C Hiller, Sebastian Bader, Devesh Singh, Thomas Kirste, Martin Becker, Martin Dyrba

German Conference on Medical Image Computing (BVM)

DOI Code

JAD

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

Devesh Singh et al.

Journal of Alzheimer’s Disease

DOI Code

Front. Neuroinform.

Contrastive self-supervised learning for neurodegenerative disorder classification

Vadym Gryshchuk, Devesh Singh, Stefan Teipel, Martin Dyrba

Frontiers in Neuroinformatics

DOI Code

BVM

Comparison of CNN Architectures for Detecting Alzheimer’s Disease using Relevance Maps

Devesh Singh, Martin Dyrba

German Conference on Medical Image Computing (BVM)

DOI Code

Academic Services

Contact Details

Address: Ernst-Heydemann-Str. 6, 18057 Rostock, Germany
Office Location: Universitätsmedizin Rostock: Biomedicum
Email: devesh.singh (at) med.uni-rostock.de