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Segmentation of clinical structures from images of the human pelvic area
The radiotherapy treatment planning requires the delineation of the therapy structures that will be submitted to the radiation beams. When executed manually, this delineation is a slow process andExpand
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A Semi-Supervised Self-Organizing Map for Clustering and Classification
There has been an increasing interest in semisupervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples.Expand
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Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering
  • H. F. Bassani, A. Araújo
  • Computer Science, Medicine
  • IEEE Transactions on Neural Networks and Learning…
  • 1 March 2015
Subspace clustering is the task of identifying clusters in subspaces of the input dimensions of a given dataset. Noisy data in certain attributes cause difficulties for traditional clusteringExpand
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MOEA/D with uniformly randomly adaptive weights
When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributedExpand
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Dimension Selective Self-Organizing Maps for clustering high dimensional data
High dimensional datasets usually present several dimensions which are irrelevant for certain clusters while they are relevant to other clusters. These irrelevant dimensions bring difficulties to theExpand
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Dynamic topology and relevance learning SOM-based algorithm for image clustering tasks
Abstract In this paper, the task of unsupervised visual object categorization (UVOC) is addressed. We utilize a variant of Self-organizing Map (SOM) to cluster images in two different scenarios:Expand
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Online incremental supervised growing neural gas
Online learning algorithms are intrinsically designed to deal with large amounts of data because of the one-instance-at-a-time approach to the learning process, circumventing memory issues andExpand
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Surface Reconstruction Method Based on a Growing Self-Organizing Map
This work introduces a method that produces triangular mesh representation of a target object surface. The new surface reconstruction method is based on Growing Self-organizing Maps, which learnsExpand
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Watershed transform for automatic image segmentation of the human pelvic area
We propose a new system for automatic image segmentation of organs of the human pelvic area. The algorithm is based on multi-region growing followed by a watershed transform. The main contributionsExpand
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Learning vector quantization with local adaptive weighting for relevance determination in Genome-Wide association studies
In Genome-Wide Association Studies (GWAS) huge amounts of genetic information are analyzed in order to discover how the observed variations, more specifically, the Single Nucleotide PolymorphismsExpand
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