Paper Info
Title
Medical Image Classification with Weighted Latent Semantic Tensors and Deep Convolutional Neural Networks.
Abstract
This paper proposes a novel approach for identifying the modality of medical images combining Latent Semantic Analysis (LSA) with Convolutional Neural Networks (CNN). In particular, we aim in investigating the potential of Neural Networks when images are represented by compact descriptors. To this end, an optimized latent semantic space is constructed that captures the affinity of images to each modality using a pre-trained network. The images are represented by a Weighted Latent Semantic Tensor in a lower space and they are used to train a deep CNN that makes the final classification. The evaluation of the proposed algorithm was based on the datasets from the ImageCLEF Medical Subfigure classification contest. Experimental results demonstrate the effectiveness and the efficiency of our framework in terms of classification accuracy, achieving comparable results to current state-of-the-art approaches on the aforementioned datasets.
Year
DOI
Venue
2018
10.1007/978-3-319-98932-7_8
Lecture Notes in Computer Science
Keywords
Field
DocType
Latent Semantic Analysis,Latent Semantic Tensors,Deep learning,Convolutional Neural Networks,Image classification,Modality classification
Pattern recognition,Tensor,Convolutional neural network,Computer science,Natural language processing,Artificial intelligence,Deep learning,Contextual image classification,Latent semantic analysis,Artificial neural network,Semantic space
Conference
Volume
ISSN
Citations 
11018
0302-9743
0
PageRank 
References 
Authors
0.34
17
2
Name
Order
Citations
PageRank
Spyridon Stathopoulos1243.74
Theodore Kalamboukis2518.43