Medical Image Learning with Limited and Noisy Data by Ghada Zamzmi, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman & Zhiyun Xue

Medical Image Learning with Limited and Noisy Data

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  • Genre Computers & Internet
  • Publisher Springer Nature
  • Released
  • Size 38.56 MB
  • Length 397 Pages

Description

This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.

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