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Titlebook: Medical Content-Based Retrieval for Clinical Decision Support; Second MICCAI Intern Henning Müller,Hayit Greenspan,Tanveer Syeda-Mahmo Conf

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發(fā)表于 2025-3-21 18:27:07 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Medical Content-Based Retrieval for Clinical Decision Support
副標(biāo)題Second MICCAI Intern
編輯Henning Müller,Hayit Greenspan,Tanveer Syeda-Mahmo
視頻videohttp://file.papertrans.cn/630/629134/629134.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Medical Content-Based Retrieval for Clinical Decision Support; Second MICCAI Intern Henning Müller,Hayit Greenspan,Tanveer Syeda-Mahmo Conf
描述This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2011, held in Toronto, Canada, in September 2011.The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions. The papers are divided on several topics on medical image retrieval with textual approaches, visual word based approaches, applications and multidimensional retrieval.
出版日期Conference proceedings 2012
關(guān)鍵詞3D retrieval; computer-aided diagnosis; multimodality; ontologies; semantic analysis
版次1
doihttps://doi.org/10.1007/978-3-642-28460-1
isbn_softcover978-3-642-28459-5
isbn_ebook978-3-642-28460-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2012
The information of publication is updating

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發(fā)表于 2025-3-21 21:26:52 | 只看該作者
Content-Based Retrieval in Endomicroscopy: Toward an Efficient , for Clinical Diagnosisining support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directly with respect to a perceived similarity ground truth. The
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發(fā)表于 2025-3-22 02:21:00 | 只看該作者
Biomedical Image Retrieval Using Multimodal Context and Concept Feature Spacestering. For content-based image search, concepts derived from visual features are modeled using support vector machine (SVM)-based classification of local patches from local image regions. Text keywords from associated metadata provides the context and are indexed using the vector space model of inf
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Building Implicit Dictionaries Based on Extreme Random Clustering for Modality Recognitione, we propose to rely on a representation of images in terms of words from a visual dictionary. To this end, we introduce a very fast approach that allows the learning of implicit dictionaries which permits the construction of compact and discriminative bag of visual words. Instead of a unique compu
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Using Multiscale Visual Words for Lung Texture Classification and Retrievallays an important role in diagnosis, as standard chest x–rays are often non–specific for ILDs. Assessment of ILDs is considerd hard for clinicians because the diseases are rare, patterns often look visually similar and various clinical data need to be integrated. An image retrieval system to support
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發(fā)表于 2025-3-23 00:39:25 | 只看該作者
Histology Image Indexing Using a Non-negative Semantic Embeddings actively investigated is the efficient and effective access to these repositories. A good access strategy demands an appropriate indexing of the collection. This paper presents a new method for indexing histology images using multimodal information, taking advantage of two kinds of data: visual da
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A Discriminative Distance Learning–Based CBIR Framework for Characterization of Indeterminate Liver (RF) similarity. Furthermore, we allow the combination of flexible user–defined semantics (in the form of retrieval contexts and high–level concepts) and appearance–based (low–level) features in order to yield search results that are both meaningful to the user and relevant in the given clinical ca
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發(fā)表于 2025-3-23 08:54:30 | 只看該作者
Computer–Aided Diagnosis of Pigmented Skin Dermoscopic Images timely and correct diagnosis of these skin lesions is one of the most important factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. In this paper we propose a machine learning approach to classify melanocytic lesions into malignant and benign f
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