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E-raamat: Data Engineering in Medical Imaging: First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

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Data Engineering in Medical Imaging: First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, ProceedingsSuurem pilt
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?Volume LNCS 14414 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada in October 2023.

The DEMI 2023 proceedings contain 11 high-quality papers of 9 to 15 pages pre-selected through a rigorous peer review process (with an average of three reviews per paper). All submissions were peer-reviewed through a double-blind process by at least three members of the scientific review committee, comprising 16 experts in the field of medical imaging. The accepted manuscripts cover various medical image analysis methods and applications.


Weakly Supervised Medical Image Segmentation through Dense Combinations
of Dense Pseudo-Labels.- Whole Slide Multiple Instance Learning for
Predicting Axillary Lymph Node Metastasis.- A Client-server Deep Federated
Learning for Cross-domain Surgical Image Segmentation.- Pre-training with
simulated ultrasound images for breast mass segmentation and
classification.- Efficient Large Scale Medical Image Dataset Preparation for
Machine Learning Applications.- A Self-supervised Approach for Detecting the
Edges of Haustral Folds in Colonoscopy Video.- Procedurally Generated
Colonoscopy and Laparoscopy Data For Improved Model Training
Performance.- Improving Medical Image Classification in Noisy Labels Using
Only Self-supervised Pretraining.- A Study on Using Transformer Encoding
Techniques to Optimize Data-driven Volume-to-Surface Registration for
Minimally Invasive Liver Interventions.- Vision Transformer-based
Self-Supervised Learning for Ulcerative Colitis Grading in
Colonoscopy.- Task-guided Domain Gap Reduction for Monocular Depth Prediction
in Endoscopy. 
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