DSIS - a database system with interrelational semantics

1981/09/09 by Zhen Yu, Xudong Jiang, Feng Zhou +8 · 33 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #AI in cancer detection #Advanced Database Systems and Queries #Algorithm #Artificial intelligence #Computer science #Contextual image classification #Convolutional neural network #Cutaneous Melanoma Detection and Management #Data Management and Algorithms #Database #Database design #Database theory #Deep learning #Discriminative model #Encoding (memory) #Image (mathematics) #Information retrieval #Kernel (algebra) #Mathematics #Pattern recognition (psychology) #Programming language #Relational database #Relational model #Representation (politics) #Residual #Semantic Web and Ontologies #Semantics (computer science) #Support vector machine #Workspace #melanin and skin pigmentation

paper · doi:10.1109/tbme.2018.2866166

published in Very Large Data Bases 66(4), 465-477 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1981/09/09 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01

Abstract

In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.

Citations

• ImageNet classification with deep convolutional neural networks

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