Titlebook: Machine Learning in Medical Imaging; 12th International W Chunfeng Lian,Xiaohuan Cao,Pingkun Yan Conference proceedings 2021 Springer Natur

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Yutong Yan,Pierre-Henri Conze,Mathieu Lamard,Heng Zhang,Gwenolé Quellec,Béatrice Cochener,Gouenou Common pathways that modulate longevity.Provides overview of r.In recent years, remarkable discoveries have been made concerning the underlying mechanisms of aging. In Life-Span Extension: Single-Cell Organisms to Man, the editors bring together a range of illuminating perspectives from researchers in

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Contrastive Representations for Continual Learning of Fine-Grained Histology Images,th limited labelling. This is of particular interest to the biomedical imaging research community, for whom the visual task is often a binary decision (healthy vs. disease) with limited quantity data and costly labelling. For such applications, the proposed method provides a light-weight option of 1

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Learning Transferable 3D-CNN for MRI-Based Brain Disorder Classification from Scratch: An Empiricalanalysis, due to small-sized samples and the domain shift problem (., caused by the use of different scanners, protocols and/or subject populations in different sites/datasets). Although previous studies proposed to pretrain CNNs on ImageNet, models’ transferability is usually limited due to semanti

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Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks, Modern population. Therefore, the early detection of knee arthritis is of great significance for diagnosis and treatment. Magnetic resonance imaging (MRI) is one of the most commonly used methods for evaluating joint degeneration in osteoarthritis research. In order to obtain information on knee ca

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Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation,ion and radiotherapy. Designing automatic segmentation methods capable of effectively exploiting the information from PET and CT as well as resolving their uncertainty remain a challenge. In this paper, we propose an lymphoma segmentation model using an UNet with an evidential PET/CT fusion layer. S

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Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning,m concatenated input images, and training is achieved by minimizing losses derived from image similarity and field regularization terms. However, the mechanism of multiresolution encoding and decoding with skip connections tends to mix up the spatial relationship between corresponding voxels or feat

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