Philips

Philips Research has long standing expertise and corresponding track record in image analysis on oncology data, coordinating and participating in previous EU-funded activities such as DR THERAPAT, iManageCancer, TRANSACT, SODA (https://soda-project.eu), and BigMedilytics (www.bigmedilytics.eu).

Additionally, Philips Research is active in the domain of AI (see https://www.philips.com/a-w/about/artificial-intelligence/ai-publications) contributing significantly to solutions in the field of diagnosis and treatment (see https://www.philips.com/a-w/about/artificial-intelligence/ai-enabled-solutions).

Philips Research is actively developing image processing tools, investigating strategies for federated AI-learning, and creating image-based AI-solutions.

Role of institution in the project

Philips Research will participate in the project by executing a validation study of the EUCAIM platform (WP 7.2) with a focus to industry perspective and requirements for usability of the platform for development purposes.

Additionally, Philips Research will provide input to the project regarding interoperability of the EUCAIM framework with Philips assets (e.g. Philips Health Suite Digital Platform) (WP5, WP6), as well as liaising with industry initiatives.   

Harald Heese

Harald Heese is a Senior Scientist at Philips Research in Hamburg, Germany. He received his diploma in Mathematics from the University of Göttingen in 2003 and graduated as Dr. rer. nat. from the University of Göttingen in 2006. He has joined Philips Research in 2006 attaining his role as Senior Scientist in 2013. Harald's research interests are in AI-based medical image analysis and in image reading workflow enhancement for oncology and cardiology applications. Harald comes with a multi-year experience in image analysis for MRI imaging, breast cancer imaging, cardiac CT imaging as well as radiation therapy optimization.

Dr. Tom Brosch

Dr. Tom Brosch is a research scientist at the Philips Research lab located in Hamburg, Germany. He earned a master's degree in Computational Visualistics from the University of Magdeburg in 2010 and Ph.D. in biomedical engineering from the University of British Columbia in 2016. His research focus is on developing machine learning-based methods for medical image understanding, in particular deep learning-based methods for segmentation of 3D CT and MR volumes. Tom is an active member of the MICCAI community with more than 50 peer-reviewed scientific publications and patents and a regular reviewer for the MICCAI conference and related workshops, which earned him an honorable mention at the outstanding reviewer awards in 2022.

Arne Ewald

Arne Ewald is an accomplished researcher with over 10 years of experience in the fields of machine learning, signal processing and computer vision for medical imaging applications. After completing his studies in computer science and engineering, he obtained a PhD from the Berlin University of Technology in the department of ‘Machine Learning and Intelligent Data Analysis’. During his doctoral research, Arne worked on developing algorithms for analyzing neuroscientific data to investigate communication principles within the human brain. Following a postdoctoral phase at the University Hospital Hamburg-Eppendorf, Arne joined Philips Research in 2015. He currently works at the department of Medical Image Processing and Analysis, where he focuses on developing algorithms for image processing and computer vision for 3D ultrasound, CT, and MR images for various applications. These algorithms include and combine model-based and data-driven approaches, mainly using artificial neural networks. Additionally, he is a trained Scrum Master and Release Train Engineer for scaled agile project management. Arne has published numerous peer-reviewed articles in high-rank scientific journals (current h-index: 10) and holds more than 20 patents. He also lectures on 'Computer Vision' at the University of Applied Science Wedel.

Dr. José Matute

Dr. José Matute works as a researcher at Philips in Germany since 2021. He received his Bachelors in Computer Engineering at the Central American Technological University in Tegucigalpa, Honduras where he worked in image segmentation techniques for calcification detection. He received his MSc from Jacobs University Bremen and his PhD from Westfälische Wilhelms-Universität Münster, Germany where he focused on cardiovascular diseases and on the etiology of non-communicable diseases within the field of medical visualization.

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Whether you’re a data holder with valuable cancer images or an innovator developing AI tools for precision medicine, this is your chance to contribute to a groundbreaking project.

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