0000000000429344

AUTHOR

Domenec Puig

showing 2 related works from this author

FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention

2019

International audience; Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel int…

General Computer ScienceComputer science02 engineering and technologyResidualFuzzy logic030218 nuclear medicine & medical imagingConvolutionconditional generative adversarial network03 medical and health sciencesSkin lesion0302 clinical medicineGradient vector flow0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceSegmentation[INFO]Computer Science [cs]channel attentionbusiness.industryresidual convolutionGeneral EngineeringPattern recognitionKernel (image processing)factorized kernel020201 artificial intelligence & image processingArtificial intelligencelcsh:Electrical engineering. Electronics. Nuclear engineeringbusinessEncoderlcsh:TK1-9971Dermoscopy images
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SIFT Texture Description for Understanding Breast Ultrasound Images

2014

Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.

medicine.diagnostic_testFeature transformbusiness.industryTexture DescriptorInformationSystems_INFORMATIONSTORAGEANDRETRIEVALComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformPattern recognitionTexture (geology)ComputingMethodologies_PATTERNRECOGNITIONmedicineDegree of similarityComputer visionArtificial intelligencebusinessBreast ultrasoundMathematics
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