Titlebook: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for; International Worksh Luping Zhou,Nich

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/l/image/581378.jpg

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Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for978-3-030-33642-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotationl pathologies in novel targets during model fitting. We achieve this using an EM algorithm: the E-step classifies surface points into pathological or healthy classes based on outliers in predicted correspondences, while the M-step performs probabilistic fitting of the statistical shape model to the

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XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosisven endangers people’s lives. In this paper, we propose a large-scale, Asian-dominated dataset of skin diseases with bounding box labels, namely XiangyaDerm. It contains 107,565 clinical images, covering 541 types of skin diseases. Each image in this dataset is labeled by professional doctors. As fa

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Weakly Supervised Segmentation from Extreme Pointstime-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extrem

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Exploring the Relationship Between Segmentation Uncertainty, Segmentation Performance and Inter-obserovements in diagnostics and patient care, especially after recent breakthroughs that have been triggered by deep learning. However, when integrating automatic tools into patient care, it is crucial to understand their limitations and to have means to assess their confidence for individual cases. Al