Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation-Reference-Cited by-同舟云学术

Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

Published:2023-11 Issue:11 Volume:42 Page:3323-3335
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Zeju¹^ORCID,Kamnitsas Konstantinos¹^ORCID,Dou Qi²^ORCID,Qin Chen³^ORCID,Glocker Ben¹^ORCID

Affiliation:

1. Department of Computing, BioMedIA Group, Imperial College London, London, U.K

2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

3. I-X and the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K

Funder

ERC under the EU's Horizon 2020 Research and Innovation Program

China Scholarship Council (CSC) Imperial Scholarship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10299534/10144095.pdf?arnumber=10144095

Reference40 articles.

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2. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

3. Explaining and harnessing adversarial examples;goodfellow;arXiv 1412 6572,2014

4. The Art of Data Augmentation

5. The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes;heller;arXiv 1904 00445,2019

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