代表
发表于 2025-3-21 18:27:07
书目名称Medical Content-Based Retrieval for Clinical Decision Support影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0629134<br><br> <br><br>书目名称Medical Content-Based Retrieval for Clinical Decision Support读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0629134<br><br> <br><br>
有花
发表于 2025-3-21 21:26:52
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
自传
发表于 2025-3-22 02:21:00
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
dictator
发表于 2025-3-22 07:53:28
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Radiation
发表于 2025-3-22 08:57:52
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
有害
发表于 2025-3-22 13:30:55
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外星人
发表于 2025-3-22 20:22:43
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
SPER
发表于 2025-3-23 00:39:25
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
JADED
发表于 2025-3-23 03:37:30
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
行业
发表于 2025-3-23 08:54:30
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