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Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. Proceedings

First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019
: Greenspan, Hayit; Tanno, Ryutaro; Erdt, Marius; Arbel, Tal; Baumgartner, Christian; Dalca, Adrian; Sudre, Carole H.; Wells, William M.; Drechsler, Klaus; Linguraru, Marius George; Oyarzun Laura, Cristina; Shekhar, Raj; Wesarg, Stefan; González Ballester, Miguel Angel


Cham: Springer Nature, 2019, XVII, 192 S.
Lecture Notes in Computer Science, 11840
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) <1, 2019, Shenzhen>
International Workshop on Clinical Image-Based Procedures (CLIP) <8, 2019, Shenzhen>
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <22, 2019, Shenzhen>
ISBN: 978-3-030-32688-3 (Print)
ISBN: 978-3-030-32689-0 (Online)
Fraunhofer IGD ()
Fraunhofer Singapore ()
artificial intelligence (AI); image processing; image segmentation; medical imaging; neural network; Lead Topic: Individual Health; Research Line: Computer graphics (CG); Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); Research Line: (Interactive) simulation (SIM); Research Line: Modeling (MOD)

On October 17, 2019, the 8th International Workshop on Clinical Image-based Procedures: From Planning to Intervention (CLIP 2019) was held in Shenzhen, China in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019). Following the tradition set in the last seven years, this year’s edition of the workshop was an exciting forum for the discussion and dissemination of clinically tested, state-of-the-art methods for image-based planning, monitoring, and evaluation of medical procedures. Nowadays, it has become more and more important for many clinical applications to base decisions not only on image data alone, thus a focus of CLIP 2019 was the creation of holistic patient models. Here, image data such as radiologic images, microscopy images, and photographs are combined with non-image information such as ‘omics’ data (e.g. genomics, proteomics), lifestyle data, demographics, EEG, and others to build a more complete picture of the individual patient and to subsequently provide better diagnosis and therapies. CLIP 2019 provided a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. Submissions related to applications already in use and evaluated by clinical users were particularly encouraged. Furthermore, novel techniques and applications that are looking at combining image analysis with clinical data mining and analytics, user studies, and other heterogeneous data were a focus as well. In CLIP’s 8th edition, world-class researchers and clinicians came together to present ways to strengthen links between computer scientists and engineers, and surgeons, interventional radiologists, and radiation oncologists.