Titlebook: Machine Learning in Medical Imaging; 10th International W Heung-Il Suk,Mingxia Liu,Chunfeng Lian Conference proceedings 2019 Springer Natur

visceral-fat

MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network,as not been fully studied in the field of deep learning within such a context. In this paper, we address the task of end-to-end segmentation based on multi-modal data and propose a novel deep learning framework, multiple subspace attention-based deep multi-modal fusion network (referred to as MSAFus

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DCCL: A Benchmark for Cervical Cytology Analysis, fields, including cervical cytology, a large well-annotated benchmark dataset remains missing. In this paper, we introduce by far the largest cervical cytology dataset, called Deep Cervical Cytological Lesions (referred to as DCCL). DCCL contains 14,432 image patches with around . pixels cropped fr

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,Children’s Neuroblastoma Segmentation Using Morphological Features,ldren. However, the automatic segmentation of NB on CT images has been addressed weakly, mostly because children’s CT images have much lower contrast than adults, especially those aged less than one year. Furthermore, neuroblastomas can develop in different body parts and are usually in variable siz

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SEED

Deep Active Lesion Segmentation,oundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities

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End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation,erformance for organ segmentation has been achieved by deep learning models, ...., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducin

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Privacy-Preserving Federated Brain Tumour Segmentation,r training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Altho