ROAM: Random layer mixup for semi‐supervised learning in medical images-Reference-Cited by-同舟云学术

ROAM: Random layer mixup for semi‐supervised learning in medical images

Published:2022-05-02 Issue:10 Volume:16 Page:2593-2608
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Bdair Tariq¹^ORCID,Wiestler Benedikt²^ORCID,Navab Nassir¹³^ORCID,Albarqouni Shadi¹⁴⁵^ORCID

Affiliation:

1. Chair for Computer Aided Medical Procedures & Augmented Reality Technical University of Munich Munich Germany

2. Department of Neuroradiology Technical University of Munich Munich Germany

3. Whiting School of Engineering Johns Hopkins University Baltimore MD USA

4. Helmholtz AI Helmholtz Center Munich Neuherberg Germany

5. Clinic for Diagnostic and Interventional Radiology University Hospital Bonn Venusberg‐Campus 1 Bonn Germany

Funder

Deutscher Akademischer Austauschdienst

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12511

Reference62 articles.

1. Fundamentals of Medical Imaging

2. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach;Aerts H.J.;Nat. Commun.,2014

3. Automated medical image segmentation techniques

4. Current Methods in Medical Image Segmentation

5. Nikolov S. Blackwell S. Zverovitch A. Mendes R. Livne M. De Fauw J. et al.:Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv:180904430 (2018)

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions;Healthcare Analytics;2024-06

2. Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks;Applied Mathematics and Nonlinear Sciences;2024-01-01

3. Cancer prediction from few amounts of histology samples through self-attention based multi-routines cross-domains network;Physics in Medicine & Biology;2023-05-22

4. Semi-supervised structure attentive temporal mixup coherence for medical image segmentation;Biocybernetics and Biomedical Engineering;2022-10

5. Guided Random Mask: Adaptively Regularizing Deep Neural Networks for Medical Image Analysis by Potential Lesions;Applied Sciences;2022-09-09