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Medtech-Joint-Venture der Hochschule Luzern

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MedTech cooperation Where Biology and Medical Engineering meet Artificial Intelligence

Using AI to make medical diagnoses more reliable and scalable is just one example of the medicine of the future we are working toward. It requires the kind of collaboration between biologists, medical engineers and IT specialists promoted in the research and development joint venture of the Lucerne University of Applied Sciences and Arts.

Will our mobile phones soon tell us what it is that makes us feel sick and what kinds of medication we should take? Will we be able to introduce automated medical services and make them available to all of humanity, including in some of the remotest areas of the world? Will early detection and the monitoring of life-threatening diseases markedly increase the patients’ cure rate and quality of life?

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Will computers be able to assist human doctors and autonomously write medical reports or make therapeutic recommendations based on image and diagnostic data as well as relevant patient information? Will virtual assistants suggest optimal treatment while taking costs, the patient’s personal preferences and the latest medical findings into account? Will we be able to instantly automate and process medical data shared between hospitals, insurers and social security to the maximum benefit of patients and medical staff? Will computers soon be able to explain their diagnoses to doctors and engage in differential diagnostics by acting as interactive sparring partners for human experts?

In fact, we are convinced that a knowledge transfer and new technologies in the fields of biology, medical engineering and artificial intelligence will make all these things possible in the near future. To advance this development, we have brought together our Competence Center for Bioscience and Medical Engineering (CC BME) and our Applied AI Research Lab in a joint venture. Its goal is to successfully address the various interdisciplinary challenges in the field of medical engineering research and development in concert with our clients.

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Core competences

We are in a position to offer our industry and business clients and our research partners a cooperation partnership in the area of applied research and development in the fields of biology, medical engineering and artificial intelligence that is fully dedicated to making holistic and sustainable progress. Consulting services, on-site trainings as well as audits and coaching in the broad fields of medical engineering, data science and artificial intelligence complete our range of services.

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Our core competences:

Precision medicine
Deterministic and stochastic mathematical modeling
Multi-omic analysis
Targeted analysis
Medical data pipelines, data technology and data science
Medical image analysis
Recommended systems technology
Natural language processing

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Infrastructure and laboratories

We operate various labs, all of which have state-of-the-art equipment and meet the highest quality and safety requirements. Cell-biological, biochemical, biotechnological and electro-physiological methods represent just some of the approaches taken at our labs.

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Our labs and research spaces:

Cell biology lab
Analytical lab
Bio safety level 2 lab
Microscopy room
and other facilities

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Collaborations

We are in constant exchange with various partner organizations in Switzerland and abroad. Together, we are headed towards the future.

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Partner organizations:

Universities of Bern and Zurich
Medical University Hannover
Swiss Center for Electronics and Microtechnology (CSEM)
Konrad Zuse Center for Information Technology Berlin
European Space Agency (ESA)University Hospitals of Basel, Bern and Zurich
Swiss4ward, Zurich
F. Hoffmann-La Roche AG, Basel
Derma.one
as well as numerous other partner organizations

We are looking forward to discovering and exploring relevant new topics with you. We will be happy to set up a meeting any time.

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Publications

PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa Ph. Gottfrois and F. Gröger and H. Andriambololoniaina and L. Amruthalingam and M. Rakotoarisaona and Gonzales-Jimenez and S. Lionetti and W. Ng and Ch. Hsu and A. Kessy and M. Pouly and D. Mavura and D. Faith and I. Traoré and R. Rapelanoro and A. Navarini MICCAI'24: Proceedings of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention Marrakesh, Marocco, 2024.

Gonzales-Jimenez, Alvaro; Lionetti, Simone; Pouly, Marc & Navarini, Alexander (2023). SANO: Score-based Diffusion Model for Anomaly Localization in Dermatology. Proceedings of CVPR 2023 Workshop on Visual Anomaly and Novelty Detection, 1.

Gonzalez-Jimenez, Alvaro; Lionetti, Simone; Gottfrois, Philippe Léon-Marius; Gröger, Fabian; Pouly, Marc & Navarini, Alexander (2023). Robust T-Loss for Medical Image Segmentation. MICCAI'23: Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention.

Gröger, Fabian; Lionetti, Simone; Gottfrois, Philippe Léon-Marius; Gonzales-Jimenez, Alvaro; Groh, Matthew; Daneshjou, Roxana; Consortium, Labelling; Navarini, Alexander & Pouly, Marc (2023). Towards Reliable Dermatology Evaluation Benchmarks. Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:101-128, 2023.

Gröger, Fabian; Gottfrois, Philippe Léon-Marius; Amruthalingam, Ludovic; Gonzalez-Jimenez, Alvaro; Lionetti, Simone; Navarini, Alexander & Pouly, Marc (2022). Towards Reducing The Need For Annotations In Digital Dermatology With Self-supervised Learning. ECAI’22: European Conference on Artificial Intelligence, Proceedings of Workshop 31: Scarce Data in Artificial Intelligence for Healthcare, 11-16. doi: 10.5220/0000164800003523

Wullschleger, Pascal; Lionetti, Simone; Daly, Donnacha; Volpe, Francesca & Caro, Grégoire (2022). Auto-Regressive Self-Attention Models for Diagnosis Prediction on Electronic Health Records. Proceedings of the 2022 IEEE International Conference on Big Data, 1.

Lefebvre, Marie; Hengartner, Michael P.; Tronci, Enrico; Mancini, Toni; Ille, Fabian; Röblitz, Susanna; Krüger, Tillmann; Leeners, Brigitte (2022). Food preferences throughout the menstrual cycle – A computer-assisted neuro-endocrino-psychological investigation.

Amruthalingam, Ludovic; Buerzle, Oliver; Gonzalez Jimenez, Alvaro; Gottfrois, Philippe Léon-Marius; Koller, Thomas; Navarini, Alexander; Pouly, Marc & Roth, Anastasia (2022). Quantification of Efflorescences in Pustular Psoriasis using Deep Learning. Healthcare Informatics Research, 2022(Volume 28(3)), 222-230. doi: 10.4258/hir.2022.28.3.222

Amruthalingam, Ludovic; Gökduman, Bulus; Gonzalez-Jimenez, Alvaro; Gottfrois, Philippe Léon-Marius; Koller, Thomas; Kunz, Michael; Navarini, Alexander & Pouly, Marc (2022). Improved Diagnosis by Automated Macro- and Micro-anatomical Region Mapping of Skin Photographs. Journal of European Academy of Dermatology Venereology, 2022, 10. doi: 10.1111/jdv.18476

Furger Fabian; Amruthalingam Ludovic; Navarini Alexander A.; Pouly Marc (2020). Applications of Generative Adversarial Networks to Dermatologic Imaging (Proceedings of the ANNPR’20: 9th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition).

Wuest Simon L.; Arnold Jaro; Gander Sarah; Zumbühl Christoph; Jost Christian; Giger-Lange Christina; Cerretti Geraldine; Caliò Martina; Rattenbacher-Kiser Karin; Follonier Cindy; Schälli Othmar; Székely Gerhard S.; Egli Marcel; Ille Fabian (2020). Microtubules and Vimentin Fiber Stability during Parabolic Flights (Microgravity Science and Technology). PDF (8.6 MB)

Pouly Marc; Koller Thomas; Gottfrois Philippe; Lionetti Simone (2020). Künstliche Intelligenz in der Bildanalyse – Grundlagen und neue Entwicklungen (Der Hausarzt). PDF (1.0 MB)

Meienberger N.; F. Anzengruber F.; Amruthalingam L.; Christen R.; Koller T.; Maul J. T.; Pouly M.; Djamei V.; Navarini A. A. (2019). Observer-independent assessment of psoriasis-affectedarea using machine learning (Journal of The European Academy of Dermatology and Venereology). PDF (477.0 KB)

Leeners Brigitte; Krüger Tillmann H. C.; Geraedts Kirsten; Tronci Enrico; Mancini Toni; Egli Marcel; Röblitz Susanna; Saleh Lanja; Spanaus Katharina; Schippert Cordula; Zhang Yuanyuan; Ille Fabian (2019). Associations Between Natural Physiological and Supraphysiological Estradiol Levels and Stress Perception (Frontiers in Behavioral Neuroscience). PDF (494.7 KB)

Hengartner Michael P.; Krüger Tillmann H. C.; Geraedts Kirsten; Tronci Enrico; Mancini Toni; Ille Fabian; Egli Marcel; Röblitz Susanna; Ehrig Rainald; Saleh Lanja; Spanaus Katharina; Schippert Cordula; Zhang Yuanyuan; Leeners Brigitte (2017). Negative affect is unrelated to fluctuations in hormone levels across the menstrual cycle: Evidence from a multisite observational study across two successive cycles (Journal of Psychosomatic Research). PDF (282.1 KB)

Leeners Brigitte; Krüger Tillmann H. C.; Geraedts Kirsten; Tronci Enrico; Mancini Toni; Ille Fabian; Egli Marcel; Röblitz Susanna; Saleh Lanja; Spanaus Katharina; Schippert Cordula; Zhang Yuanyuang; Hengartner Michael P. (2017). Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles (Frontiers in Behavioral Neuroscience). PDF (723.2 KB)

Tronci E.; Mancini T.; Salvo I.; SinisiS.; Mari F.; Melatti I.; Massini A.; Davi F.; Dierkes T.; Ehrig R.; Röblitz S.; Leeners B.; Krüger T. H. C.; Egli M.; Ille F. (2014). Patient-Specific Models from Inter-Patient Biological Models and Clinical Records (Formal Methods in Computer-Aided Design). PDF

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