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Titlebook: Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales fo; 1st International Wo John S. H. Baxte

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發(fā)表于 2025-3-23 10:02:55 | 只看該作者
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發(fā)表于 2025-3-23 15:20:23 | 只看該作者
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發(fā)表于 2025-3-23 23:41:20 | 只看該作者
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發(fā)表于 2025-3-24 02:25:07 | 只看該作者
Geophysics and Astrophysics Monographsdly, a regularization loss term is proposed by integrating assumptions about the cortical thickness within each sample. Our experiments on the developing human connectome project (dHCP) dataset show that our method can predict accurate CGM segmentation learned from noisy labels.
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發(fā)表于 2025-3-24 10:25:44 | 只看該作者
Conference proceedings 2022022); the 12.th. International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support (ML-CDS 2022) and the 2.nd. International Workshop on Topological Data Analysis for Biomedical Imaging (TDA4BiomedicalImaging 2022), held in conjunction with the 25th International C
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發(fā)表于 2025-3-24 11:41:04 | 只看該作者
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發(fā)表于 2025-3-24 17:54:00 | 只看該作者
Richard A. Register,Robert K. Prud’hommer, we describe the user-centered design process and give an example of how it can be incorporated to enhance the development of surgical innovations. As a case study, we focus on one of the most commonly performed and error-prone neurosurgical procedures, ventriculostomy.
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發(fā)表于 2025-3-24 21:48:14 | 只看該作者
N. V. Smirnova,O. F. Ogloblina,V. A. Vlaskovtperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.
20#
發(fā)表于 2025-3-25 01:01:03 | 只看該作者
A 35-Year Longitudinal Analysis of?Dermatology Patient Behavior Across Economic and Cultural Manifeselp address the issues identified across economic and cultural manifestations. Our analysis is further framed around three types of digital tools: “Dr. Google”, social media, and artificial intelligence (AI) tools, and across three stages of clinical care: pre-visit, in-visit, and post-visit.
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