Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 [electronic resource] : 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part V / edited by Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li.
Contributor(s): Wang, Linwei [editor.]
| Dou, Qi [editor.] | Fletcher, P. Thomas [editor.] | Speidel, Stefanie [editor.] | Li, Shuo [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 13435Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: XL, 738 p. 253 illus., 224 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031164439.Subject(s): Image processing | Machine learning | Application software | Image processing -- Digital techniques | Computer vision | Information technology -- Management | Image Processing | Machine Learning | Computer and Information Systems Applications | Computer Imaging, Vision, Pattern Recognition and Graphics | Computer Application in Administrative Data ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
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Springer Nature eBookSummary: The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning - domain adaptation and generalization; Part VIII: Machine learning - weakly-supervised learning; machine learning - model interpretation; machine learning - uncertainty; machine learning theory and methodologies. .
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning - domain adaptation and generalization; Part VIII: Machine learning - weakly-supervised learning; machine learning - model interpretation; machine learning - uncertainty; machine learning theory and methodologies. .
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