Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Released on by Qian Wang
preview-18

  • Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data Book Detail

  • Author : Qian Wang
  • Release Date : 2019-10-13
  • Publisher : Springer Nature
  • Genre : Computers
  • Pages : 254
  • ISBN 13 : 3030333914
  • File Size : 81,81 MB
DOWNLOAD BOOK

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data by Qian Wang PDF Summary

Book Description: 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.

Disclaimer: www.yourbookbest.com does not own Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis

File Size : 87,87 MB
Total View : 2760 Views
DOWNLOAD

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses