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Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. 10th International Workshop, ML-CDS 2020 and 9th International Workshop, CLIP 2020. Proceedings

Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020
 
: Syeda-Mahmood, Tanveer; Drechsler, Klaus; Greenspan, Hayit; Madabhushi, A.; Karargyris, A.; Oyarzun Laura, Cristina; Linguraru, M.G.; Wesarg, Stefan; Shekhar, R.; Erdt, Marius; Gonzàlez Ballester, M.A.

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Cham: Springer Nature, 2020, XII, 138 pp.
Lecture Notes in Computer Science, 12445
International Workshop on Clinical Image-Based Procedures (CLIP) <9, 2020, Online>
International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS) <10, 2020, Online>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <23, 2020, Online>
ISBN: 978-3-030-60945-0 (Print)
ISBN: 978-3-030-60946-7 (Online)
English
Conference Proceedings
Fraunhofer IGD ()
Fraunhofer Singapore ()
artificial intelligence (AI); image processing; Bioinformatics; Lead Topic: Individual Health; Research Line: Computer vision (CV)

Abstract
On October 4, 2020, the 9th International Workshop on Clinical Image-based Procedures: From Planning to Intervention (CLIP 2020), was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Due to the COVID-19 pandemic, the workshop was held as an online-only meeting to contribute to slowing down the spread of the virus. Despite the challenges involved, we have continued to build on what we have successfully practiced over the past eight years: providing a platform for the dissemination of clinically tested, state-of-the-art methods for image-based planning, monitoring, and evaluation of medical procedures. A major focus of CLIP 2020 was on the creation of holistic patient models to better understand the need of the individual patient and thus provide better diagnoses and therapies. In this context, it is becoming increasingly important to not only base decisions on image data alone, but to combine these with non-image data, such as ‘omics’ data, electronic medical records, electroencephalograms, and others. This approach offers exciting opportunities to research. CLIP provides a platform to present and discuss these developments and work, centered on specific clinical applications already in use and evaluated by clinical users. In 2020, CLIP accepted nine original manuscripts from all over the world for oral presentation at the online event. Each of the manuscripts underwent a single-blind peer review by two members of the Program Committee, all of them prestigious experts in the field of medical image analysis and clinical translations of technology. We would like to thank our Program Committee for its invaluable contributions and continuous support of CLIP over the years. It is not always easy to find the time to support our workshop given full schedules and challenges due to the ongoing pandemic, and we are very grateful to all our members because CLIP 2020 would not have been possible without them. We would also like to thank all the authors for their high-quality contributions this year as well as their efforts to make CLIP 2020 a success. Finally, we would like to thank all MICCAI 2020 organizers for supporting the organization of CLIP 2020.

: http://publica.fraunhofer.de/documents/N-633922.html