Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation

Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation

Digitization techniques for biomedical images yield disparate visual patterns in radiological exams. These pattern differences, which can be viewed as a domain-shift problem, may hamper the use of data-driven approaches for inference over these images, such as Deep Neural Networks. Another noticeabl...

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Bibliographic Details
Main Authors: Hugo N. Oliveira, Edemir Ferreira, Jefersson A. Dos Santos
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep learning
domain adaptation
biomedical images
semantic segmentation
image translation
semi-supervised learning
Online Access:https://ieeexplore.ieee.org/document/9084116/

Internet

https://ieeexplore.ieee.org/document/9084116/

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