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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and Fi [Paperback]

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  • Category: Books (Computers)
  • ISBN-10:  3030333906
  • ISBN-10:  3030333906
  • ISBN-13:  9783030333904
  • ISBN-13:  9783030333904
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Apr-2019
  • Pub Date:  01-Apr-2019
  • SKU:  3030333906-11-SPRI
  • SKU:  3030333906-11-SPRI
  • Item ID: 105244500
  • List Price: $54.99
  • Seller: ShopSpell
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This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.

MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. 

DART 2019.- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation.- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations.- Multi-layer Domain Adaptation for Deep Convolutional Networks.- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training.- Learning Interpretable Disentangled Representations using Adversarial VAEs.- Synthesising Images and Labels Between MR Sequence Types With CycleGAN.- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning.- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans.- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection.- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images.- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases.- Generating Virtual Chromoendoscopic Imagesand Improving Detectability and Classification Performance of Endoscopic Lesions.- MIL3ID 2019.- Self-supervised leală§

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