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.
Medical Image Analysis with Normative Machine Learning
(Third Party Funds Single)
Project leader: Bernhard Kainz Term: 1. September 2023 - 30. September 2028 Acronym: ERC-CoG MIA-NORMAL Funding source: Europäische Union (EU)
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.
Erick, F., Müller, J., Li, Z., & Kainz, B. (2026). Last Layer Laplacian Pseudocoresets for Robust Medical Image Analysis. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 278-287). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
Müller, J., Wright, R., Day, T.G., Venturini, L., Budd, S.F., Reynaud, H.,... Kainz, B. (2026). L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation. In Dong Ni, Ruobing Huang, Wufeng Xue, Alison Noble (Eds.), Lecture Notes in Computer Science (pp. 164-173). Daejeon, KR: Springer Science and Business Media Deutschland GmbH.
Nützel, F., Dombrowski, M.N., & Kainz, B. (2026). Ontology-Based Concept Distillation for Radiology Report Retrieval and Labeling. In Zhiming Cui, Islem Rekik, Heung-IL Suk, Xi Ouyang, Kaicong Sun, Sheng Wang (Eds.), Lecture Notes in Computer Science (pp. 540-550). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
Qiao, M., Zheng, J., Zhang, W., Ma, Q., Li, L., Kainz, B.,... Bai, W. (2026). Mesh4D: A Motion-Aware Multi-view Variational Autoencoder for 3D+t Mesh Reconstruction. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Jinah Park, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 343-353). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
Zhang, W., Qiao, M., Zang, C., Niederer, S., Matthews, P.M., Bai, W., & Kainz, B. (2026). Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 429-439). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
2025
Dombrowski, M.N., & Kainz, B. (2025). Enabling PSO-Secure Synthetic Data Sharing Using Diversity-Aware Diffusion Models. In Ghada Zamzmi, Annika Reinke, Ravi Samala, Meirui Jiang, Xiaoxiao Li, Holger Roth, Mariia Sidulova, Thijs Kooi, Shadi Albarqouni, Spyridon Bakas, Nicola Rieke (Eds.), Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning. First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings (pp. 25-35). Daejeon, KR: Cham: Springer.
Dombrowski, M.N., Zhang, W., Cechnicka, S., Reynaud, H., & Kainz, B. (2025). Image Generation Diversity Issues and How to Tame Them. In Proceedings of the Computer Vision and Pattern Recognition Conference 2025 (pp. 3029-3039). Nashville, TN, USA: The Computer Vision Foundation.
Erick, F., Mina, R., Müller, J., & Kainz, B. (2025). Uncertainty-Aware Vision Transformers for Medical Image Analysis. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings (pp. 171).
Fischer, L.K., Müller, J., Schröder, C., Hanser, A., Cuomo, M., Day, T.,... Kainz, B. (2025). Unsupervised Single-source Domain Generalization for Robust Quantification of Lymphatic Perfusion. In Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2025. Book SubtitleProceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 (pp. 178-184). Regensburg, DE: Cham: Springer.
Li, Z., Zhang, W., Cechnicka, S., & Kainz, B. (2025). Data-Efficient Generation for Dataset Distillation. In Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi (Eds.), Lecture Notes in Computer Science (pp. 68-82). Milan, ITA: Springer Science and Business Media Deutschland GmbH.
Müller, J., & Kainz, B. (2025). Resource-Efficient Medical Image Analysis with Self-adapting Forward-Forward Networks. In Xuanang Xu, Zhiming Cui, Kaicong Sun, Islem Rekik, Xi Ouyang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 180-190). Marrakesh, MA: Springer Science and Business Media Deutschland GmbH.
Müller, J., Knupfer, A., Blöss Braga, P., Vittur, E.B., Kainz, B., & Hutter, J. (2025). Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models. In M. Emre Celebi, Johanna Paula Müller, Catarina Barata, Allan Halpern, Philipp Tschandl, Marc Combalia, Yuan Liu, Kumar Abhishek, Joanna Jaworek-Korjakowska, Moi Hoon Yap, Katharina Breininger, Maximilian Lindholz, Jana Hutter, Richard Ruppel, Smiti Tripathy, Franziska Mathis-Ullrich, Stefanie Burghaus, Matthias May (Eds.), Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health. 10th International Workshop, ISIC 2025, and First International Workshop, CAPI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings (pp. 93-102). Daejeon, KR: Cham: Springer.
Vlontzos, A., Müller, C., & Kainz, B. (2025). Causal reasoning in medical imaging. In Trustworthy AI in Medical Imaging. (pp. 367-381). Academic Press.
Öttl, M., Wilm, F., Steenpaß, J., Qiu, J., Rübner, M., Hartmann, A.,... Breininger, K. (2025). Style-Extracting Diffusion Models for Semi-supervised Histopathology Segmentation. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 236-252). Milan, IT: Springer Science and Business Media Deutschland GmbH.
Basaran, B.D., Zhang, W., Qiao, M., Kainz, B., Matthews, P.M., & Bai, W. (2024). LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation. In Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 73-83). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
Cechnicka, S., Ball, J., Baugh, M., Reynaud, H., Simmonds, N., Smith, A.P.,... Kainz, B. (2024). URCDM: Ultra-Resolution Image Synthesis in Histopathology. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 535-545). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
Cechnicka, S., Ball, J., Reynaud, H., Arthurs, C., Roufosse, C., & Kainz, B. (2024). Realistic Data Enrichment for Robust Image Segmentation in Histopathology. In Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 63-72). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
Dombrowski, M.N., Reynaud, H., Müller, J., Baugh, M., & Kainz, B. (2024). Trade-Offs in Fine-Tuned Diffusion Models between Accuracy and Interpretability. In Association for the Advancement of Artificial Intelligence (Eds.), AAAI-24 Special Track Safe, Robust and Responsible AI Track (pp. 21037-21045). Vancouver, CA: Washington, DC: AAAI Press.
Grzech, D., Folgoc, L.L., Azampour, M.F., Vlontzos, A., Glocker, B., Navab, N.,... Kainz, B. (2024). Unsupervised Similarity Learning for Image Registration with Energy-Based Models. In Marc Modat, Žiga Špiclin, Alessa Hering, Ivor Simpson, Wietske Bastiaansen, Tony C. W. Mok (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 229-240). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
Li, L., Wang, H., Baugh, M., Ma, Q., Zhang, W., Ouyang, C.,... Kainz, B. (2024). Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 670-680). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
Paraperas, P.F., Alexandros, L., Stylianos, M., Deng, J., Kainz, B., & Stefanos, Z. (2024). Arc2face: A foundation model for id-consistent human faces. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 241-261). Springer.
Stegmaier, M., Schröder, C., Müller, J., Day, T., Cuomo, M., Dewald, O.,... Kainz, B. (2024). Automatic Segmentation of Lymphatic Perfusion in Patients with Congenital Single Ventricle Defects. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 255-260). Erlangen, DE: Wiesbaden: Springer Vieweg.
Baugh, M., Tan, J., Müller, J.P., Dombrowski, M., Batten, J., & Kainz, B. (2023). Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 162-172). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
Kainz, B., Noble, J.A., Schnabel, J., Khanal, B., Müller, J., & Day, T. (Eds.) (2023). Simplifying Medical Ultrasound. Cham: Springer.
Li, L., Ma, Q., Ouyang, C., Li, Z., Meng, Q., Zhang, W.,... Kainz, B. (2023). Robust Segmentation via Topology Violation Detection and Feature Synthesis. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 67-77). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.
Ma, Q., Li, L., Kyriakopoulou, V., Hajnal, J.V., Robinson, E.C., Kainz, B., & Rueckert, D. (2023). Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 312-322). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.
Müller, J., Baugh, M., Tan, J., Dombrowski, M.N., & Kainz, B. (2023). Confidence-Aware and Self-supervised Image Anomaly Localisation. In Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Raghav Mehta, Chen Qin, William M. Wells (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 177-187). Vancouver, CA: IEEE.
Reynaud, H., Qiao, M., Dombrowski, M.N., Day, T., Razavi, R., Gomez, A.,... Kainz, B. (2023). Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 142-152). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.
Schmidtke, L., Hou, B., Vlontzos, A., & Kainz, B. (2023). Self-supervised 3D Human Pose Estimation in Static Video via Neural Rendering. In Leonid Karlinsky, Tomer Michaeli, Ko Nishino (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 704-713). Tel Aviv, ISR: Springer Science and Business Media Deutschland GmbH.
Shkëmbi, G., Müller, J., Li, Z., Breininger, K., Schüffler, P., & Kainz, B. (2023). Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. In Binod Bhattarai, Sharib Ali, Anita Rau, Anh Nguyen, Ana Namburete, Razvan Caramalau, Danail Stoyanov (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 11-20). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.
Zhang, W., Basaran, B., Meng, Q., Baugh, M., Stelter, J., Lung, P.,... Kainz, B. (2023). MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI. In Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 121-131). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.
Baugh, M., Tan, J., Vlontzos, A., Müller, J., & Kainz, B. (2022). nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods. In Carole H. Sudre, Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Adrian Dalca, William M. Wells III, Chen Qin, Ryutaro Tanno, Koen Van Leemput, Koen Van Leemput, William M. Wells III (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 103-112). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
Grzech, D., Azampour, M.F., Glocker, B., Schnabel, J., Navab, N., Kainz, B., & Folgoc, L.L. (2022). A variational Bayesian method for similarity learning in non-rigid image registration. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 119-128). New Orleans, LA, US: IEEE Computer Society.
Lebbos, C., Barcroft, J., Tan, J., Müller, J., Baugh, M., Vlontzos, A.,... Kainz, B. (2022). Adnexal Mass Segmentation with Ultrasound Data Synthesis. In Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 106-116). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
Li, L., Ma, Q., Li, Z., Ouyang, C., Zhang, W., Price, A.,... Alansary, A. (2022). Fetal Cortex Segmentation with Topology and Thickness Loss Constraints. In ETHICAL AND PHILOSOPHICAL ISSUES IN MEDICAL IMAGING, MULTIMODAL LEARNING AND FUSION ACROSS SCALES FOR CLINICAL DECISION SUPPORT, AND TOPOLOGICAL DATA ANALYSIS FOR BIOMEDICAL IMAGING, EPIMI 2022, ML-CDS 2022, TDA4BIOMEDICALIMAGING (pp. 123-133). Singapore, SINGAPORE: CHAM: SPRINGER INTERNATIONAL PUBLISHING AG.
Ouyang, C., Wang, S., Chen, C., Li, Z., Bai, W., Kainz, B., & Rueckert, D. (2022). Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation. In Carole H. Sudre, Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Adrian Dalca, William M. Wells III, Chen Qin, Ryutaro Tanno, Koen Van Leemput, Koen Van Leemput, William M. Wells III (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 59-69). Singapore, SG: Springer Science and Business Media Deutschland GmbH.
Reynaud, H., Vlontzos, A., Dombrowski, M.N., Gilligan Lee, C., Beqiri, A., Leeson, P., & Kainz, B. (2022). D’ARTAGNAN: Counterfactual Video Generation. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 599-609). Singapore, SG: Springer Science and Business Media Deutschland GmbH.
Schlüter, H.M., Tan, J., Hou, B., & Kainz, B. (2022). Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 474-489). Tel Aviv, IL: Springer Science and Business Media Deutschland GmbH.
Tan, J., Kart, T., Hou, B., Batten, J., & Kainz, B. (2022). MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision. In Marc Aubreville, David Zimmerer, Mattias Heinrich (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 119-126). Strasbourg, FRA: Springer Science and Business Media Deutschland GmbH.
Kainz, B. (2021). CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI. In Proceedings of the 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 (pp. 221-230). Springer Science and Business Media Deutschland GmbH.
Schmidtke, L., Vlontzos, A., Ellershaw, S., Lukens, A., Arichi, T., & Kainz, B. (2021). Unsupervised Human Pose Estimation through Transforming Shape Templates. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2484-2494). Virtual, Online, USA: IEEE Computer Society.
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.
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.
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
Medical Image Analysis with Normative Machine Learning
(Third Party Funds Single)
Term: 1. September 2023 - 30. September 2028
Acronym: ERC-CoG MIA-NORMAL
Funding source: Europäische Union (EU)
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
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