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Titlebook: Ophthalmic Medical Image Analysis; 7th International Wo Huazhu Fu,Mona K. Garvin,Yalin Zheng Conference proceedings 2020 Springer Nature Sw

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书目名称Ophthalmic Medical Image Analysis
副标题7th International Wo
编辑Huazhu Fu,Mona K. Garvin,Yalin Zheng
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Ophthalmic Medical Image Analysis; 7th International Wo Huazhu Fu,Mona K. Garvin,Yalin Zheng Conference proceedings 2020 Springer Nature Sw
描述This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually due to the COVID-19 crisis..The 21 papers presented at OMIA 2020 were carefully reviewed and selected from 34 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies..
出版日期Conference proceedings 2020
关键词artificial intelligence; color image processing; color images; computer vision; computer-aided detection
版次1
doihttps://doi.org/10.1007/978-3-030-63419-3
isbn_softcover978-3-030-63418-6
isbn_ebook978-3-030-63419-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder,4% and 95.73% dice value for OD segmentation on ADAM and REFUGE data, respectively. For fovea detection, the average Euclidean distance of 26.17 pixels is achieved for the ADAM dataset. The proposed method stood first for OD detection and segmentation tasks in ISBI ADAM 2020 challenge.
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Weakly Supervised Retinal Detachment Segmentation Using Deep Feature Propagation Learning in SD-OCTfor cross-validation experiments. The experimental results demonstrate that the proposed method can achieve encouraging segmentation accuracy comparable to strong supervision methods only utilizing image-level labels.
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A Framework for the Discovery of Retinal Biomarkers in Optical Coherence Tomography Angiography (OCtinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.
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An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitance the feature extraction capability of the network. The HBP module can complement the information of the feature layers to capture the feature relationship between the layers so that the representation ability of the model can be enhanced. Finally, in order to solve the imbalance problem of AP-ROP
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