Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

Jukić, Ante; Kopriva, Ivica; Cichocki, Andrzej

doi:10.1016/j.bspc.2013.07.001

Cited by 14 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For instance, [36] proposed a model reduction and feature extraction for large-scale problems. [37] proposed a feature extraction method suitable for color medical images. [38] proposed an NTD-based framework to extract urban mobility patterns from a massive taxi trajectory data set in Beijing Analogously, [39] applied an NTD model to decompose high-dimensional mobility data into a meaningful pattern.…”

Section: Pattern Extraction From Georeferenced Datamentioning

confidence: 99%

TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization

Silveira¹,

García²,

Paiva³

et al. 2022

Preprint

View full text Add to dashboard Cite

Extracting relevant urban patterns from multiple data sources can be difficult using classical clustering algorithms since we have to make a suitable setup of the hyperparameters of the algorithms and deal with outliers. It should be addressed correctly to help urban planners in the decision-making process for the further development of a big city. For instance, experts' main interest in criminology is comprehending the relationship between crimes and the socio-economics characteristics at specific georeferenced locations. In addiction, the classical clustering algorithms take little notice of the intricate spatial correlations in georeferenced data sources. This paper presents a new approach to detecting the most relevant urban patterns from multiple data sources based on tensor decomposition. Compared to classical methods, the proposed approach's performance is attested to validate the identified patterns' quality. The result indicates that the approach can effectively identify functional patterns to characterize the data set for further analysis in achieving good clustering quality. Furthermore, we developed a generic framework named TensorAnalyzer, where the effectiveness and usefulness of the proposed methodology are tested by a set of experiments and a real-world case study showing the relationship between the crime events around schools and students performance and other variables involved in the analysis.

show abstract

Section: Pattern Extraction From Georeferenced Datamentioning

confidence: 99%

TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization

Silveira¹,

García²,

Paiva³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…All of these algorithms are designed to conduct the image processing of gray scale in one dimension. However, during surgery and diagnosis [8], doctors must be able to pinpoint the color image; the classification of data must effectively process color pictures [9][10][11][12][13]. Feature extraction in color images requires the development of new extraction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Color Images

Han

2016

Electronics, Communications and Networks V

View full text Add to dashboard Cite

The extraction of certain characteristics points such as color edge, inflection points, etc., is an imaging problem which requires urgent attention. This paper proposes a similar color segment algorithm. The algorithm is analyzed in different color distribution situations, and the extraction effect to the color is shown. Additionally, experimental analysis of the algorithms is provided. Experimental results indicate that the similar color segment algorithm performs better than existing algorithms in relation to a more obvious color edge, as it has better edge detection, stronger anti-noise ability, a faster processing speed and other advantages. Moreover, this paper compares the proposed algorithm to existing classical feature extraction algorithms.

show abstract

“…However, there has been a lack of literature using tensor decomposition for skin lesion classification problems. One reason behind this may be due to the heterogeneity of lesion shapes, locations, and sizes, which makes implementating tensor decomposition challenging since it is infeasible to stack the images together [42]. Therefore, this motivates us to consider a novel image registration method [69] to align the lesion information to effectively implement tensor decomposition.…”

Section: Introductionmentioning

confidence: 99%

Dermoscopic Image Classification with Neural Style Transfer

Li¹,

Zhu²,

Qu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Skin cancer, the most commonly found human malignancy, is primarily diagnosed visually via dermoscopic analysis, biopsy and histopathological examination. However, unlike other types of cancer, automated image classification of skin lesions is deemed more challenging due to the irregularity and variability in the lesions' appearances. In this work, we propose an adaptation of the Neural Style Transfer (NST) as a novel image pre-processing step for skin lesion classification problems. We represent each dermoscopic image as a style image and transfer the style of the lesion onto a homogeneous content image. This transfers the main variability of each lesion onto the same localized region, which allows us to integrate the generated images together and extract latent, low-rank style features via tensor decomposition. We train and cross-validate our model on a dermoscopic data set collected and preprocessed from the International Skin Imaging Collaboration (ISIC) database. We show that the classification performance based on the extracted tensor features using the style-transferred images significantly outperforms that of the raw images by more than 10%, and is also competitive with well-studied, pre-trained CNN models using transfer learning. Additionally, the tensor decomposition further identifies latent style clusters, which may provide clinical interpretation and insights.

show abstract

Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

Abstract: We propose a feature extraction method for noninvasive diagnosis of melanoma

Cited by 14 publications

References 49 publications

TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization

TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization

Feature Extraction for Color Images

Dermoscopic Image Classification with Neural Style Transfer

Contact Info

Product

Resources

About