Prof. Dr. Bernhard Kainz
Chair for Data, Sensors and Devices / Department Artificial Intelligence in Biomedical Engineering (AIBE)

Prof. Dr. Bernhard Kainz
Chair for Data, Sensors and Devices / Department Artificial Intelligence in Biomedical Engineering (AIBE)

My research is about intelligent algorithms in healthcare, especially Medical Imaging. I am working on self-driving medical image acquisition that can guide human operators in real-time during diagnostics. Artificial Intelligence is currently used as a blanket term to describe research in these areas.
Thus, we try to democratize rare healthcare expertise through Machine Learning, providing guidance in real-time applications and second reader expertise in retrospective analysis. We develop normative learning algorithms for large populations, integrating imaging, patient records and omics, leading to data analysis that mimics human decision making.

Research projects

  • MAVEHA: Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment — a project analysing motion patterns of neonates to identify normal or pathological neurological development.
  • iFIND: Intelligent Fetal Imaging and Diagnosis — this project aims at democratizing healthcare expertise for prenatal fetal health screening with ultrasound imaging (and some magnetic resonance imaging).
  • KIKALU: KI-geführte Kartografie und Lokalisierung für Ultraschallbildgebung — guidance through AI agents for ultrasound imaging
  • SENTINEL — Sensitive Evaluation of New Distribution Input with Normative Learning — development of normative learning algorithms for anomaly detection in medical image analysis
  • CADDI — Computer-Assisted Disease Detection in Images: translation of medical image analysis with AI into the clinical practice including federated and privacy-preserving learning.
  • RHD-Nepal: Low-cost portable AI-assisted echocardiography of Rheumatic Heart Disease by non-experts — AI can support healthcare professionals in developing countries.

  • ERC-CoG MIA-NORMAL: Medical Image Analysis with Normative Machine Learning

    (Third Party Funds Single)

    Project leader:
    Term: 1. September 2023 - 30. September 2028
    Acronym: ERC-CoG MIA-NORMAL
    Funding source: Europäische Union (EU)
    Abstract

    As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialised clinical experts and is thus rarely available at the front-line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding certain conditions or confirming physiological normality would be essential at many stages of the patient journey, to streamline referrals and relieve pressure on human experts who have limited capacity. Hence, there is a strong need for increased imaging with automated diagnostic support for clinicians, healthcare professionals, and caregivers.

    Machine learning is expected to be an algorithmic panacea for diagnostic automation. However, despite significant advances such as Deep Learning with notable impact on real-world applications, robust confirmation of normality is still an unsolved problem, which cannot be addressed with established approaches.

    Like clinical experts, machines should also be able to verify the absence of pathology by contrasting new images with their knowledge about healthy anatomy and expected physiological variability. Thus, the aim of this proposal is to develop normative representation learning as a new machine learning paradigm for medical imaging, providing patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. We will do this by developing novel Deep Learning approaches that can learn without manual labels from healthy patient data only, applicable to cross-sectional, sequential, and multi-modal data. Resulting models will be able to extract clinically useful and actionable information as early and frequent as possible during patient journeys.

2026

2025

2024

2023

2022

2021

2020

  • Budd, S., Patkee, P., Baburamani, A., Rutherford, M., Robinson, E.C., & Kainz, B. (2020). Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. In Seyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, Madhura Ingalhalikar (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-12). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Grzech, D., Kainz, B., Glocker, B., & le Folgoc, L. (2020). Image Registration via Stochastic Gradient Markov Chain Monte Carlo. In Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-12). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Hinterreiter, A., Streit, M., & Kainz, B. (2020). Projective Latent Interventions for Understanding and Fine-Tuning Classifiers. In Jaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 13-22). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Liu, T., Meng, Q., Vlontzos, A., Tan, J., Rueckert, D., & Kainz, B. (2020). Ultrasound Video Summarization Using Deep Reinforcement Learning. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 483-492). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Meng, Q., Rueckert, D., & Kainz, B. (2020). Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment. In Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 146-157). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Miolane, N., Guigui, N., Le Brigant, A., Mathe, J., Hou, B., Thanwerdas, Y.,... Pennec, X. (2020). Geomstats: A python package for riemannian geometry in machine learning. Journal of Machine Learning Research, 21.
  • Tan, J., Au, A., Meng, Q., FinesilverSmith, S., Simpson, J., Rueckert, D.,... Kainz, B. (2020). Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening. In Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 243-252). Lima, PER: Springer Science and Business Media Deutschland GmbH.
  • Tan, J., & Kainz, B. (2020). Divergent search for image classification behaviors. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 91-92). Cancun, MEX: Association for Computing Machinery, Inc.
  • Vlontzos, A., Budd, S., Hou, B., Rueckert, D., & Kainz, B. (2020). 3D Probabilistic Segmentation and Volumetry from 2D Projection Images. In Jens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 48-57). Lima, PER: Springer Science and Business Media Deutschland GmbH.

Related Research Fields