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Data Augmentation, Labelling, and Imperfections [electronic resource] : Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Hien V. Nguyen, Sharon X. Huang, Yuan Xue.

Contributor(s): Nguyen, Hien V [editor.] | Huang, Sharon X [editor.] | Xue, Yuan [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 13567Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: X, 124 p. 45 illus., 43 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031170270.Subject(s): Image processing -- Digital techniques | Computer vision | Artificial intelligence | Computers | Application software | Computer Imaging, Vision, Pattern Recognition and Graphics | Artificial Intelligence | Computing Milieux | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006 Online resources: Click here to access online
Contents:
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging -- DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images -- Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study -- Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely -- TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation -- Disentangling A Single MR Modality -- CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation -- Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning -- CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants -- A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data -- Efficient Medical Image Assessment via Self-supervised Learning -- Few-ShotLearning Geometric Ensemble for Multi-label Classification of Chest X-rays.
In: Springer Nature eBookSummary: This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.
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Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging -- DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images -- Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study -- Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely -- TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation -- Disentangling A Single MR Modality -- CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation -- Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning -- CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants -- A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data -- Efficient Medical Image Assessment via Self-supervised Learning -- Few-ShotLearning Geometric Ensemble for Multi-label Classification of Chest X-rays.

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.

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