In this paper, we propose a novel unsupervised domain adaptation framework adaptively learning feature representation to achieve cross-device lesion detection ...
In this paper, we propose a novel unsupervised domain adaptation framework for cross-device OCT lesion detection via learning adaptive features. Our main ...
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Oct 22, 2021 · Results.The model was tested on two sets of OCT image data captured from different devices, obtained an average accuracy improvement of more ...
Missing: Adaptive Features.
Unsupervised domain adaptation model for lesion detection in retinal ...
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Oct 22, 2021 · So in this study, we aimed to build an unsupervised domain adaptation lesion detection model for retinal OCT images with different ...
This paper presents a contrastive learning-based method to deal with unsupervised domain adaptation problem on the OCT segmentation task. The key technology is ...
Oct 22, 2021 · Methods. In this work, we proposed a faster-RCNN based, unsupervised domain adaptation model to address the lesion detection task in cross- ...
Sep 16, 2022 · [12] Suhui Yang et al. “Unsupervised Domain Adaptation for Cross-Device. OCT Lesion Detection via Learning Adaptive Features”. In: 2020 IEEE.
Unsupervised Domain Adaptation with Contrastive Learning for OCT ...
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Mar 7, 2022 · In this paper, we propose a multi-stage domain adaptation method (MSDA), which can learn generalized and effective domain invariant information ...
Optical coherence tomography (OCT) is a practical basis that is widely used for computer-aided retinal diagnosis, and OCT images from different devices show ...
These methods are broadly categorized into six classes: Adversarial feature learning, Pseudo-label based self-training, Graph-reasoning, Image-to-image ...