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

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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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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

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Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Hisms are based on the assumption that the nuclei center should have larger responses than their surroundings in the probability map of the pathological image, which in turn transforms the detection or localization problem into finding the local maxima on the probability map. However, all the existing

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Semi-supervised Multi-task Learning with Chest X-Ray Images,ntrast, generative modeling—i.e., learning data generation and classification—facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model