Online Variational Learning for Medical Image Data Clustering

Kalra, Meeta; Osadebey, Michael; Bouguila, Nizar; Pedersen, Marius; Fan, Wentao

doi:10.1007/978-3-030-23876-6_11

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…For example, the simple matching distance is used to measure the similarity of two categorical data points; a general similarity coefficient is adopted to measure the distance of two mixed-type data points or the means of two clusters; probabilistic model, landmark models, and time series transformation distance are used to measure the similarity of two time-series data points. In particular, Wu et al [2] considered spectral clustering for high-dimensional data exploiting sparse representation vectors, Kalra et al [3] presented online variational learning for medical image data clustering, Prasad et al [4] discussed leveraging variational autoencoders for image clustering, Soleymani et al [5] proposed a deep variational clustering framework for self-labeling of large-scale medical image data.…”

Section: Measures Of Similarity or Dissimilaritymentioning

confidence: 99%

Introductory Chapter: Development of Data Clustering

Tang¹,

Wu²

2022

Data Clustering

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Section: Measures Of Similarity or Dissimilaritymentioning

confidence: 99%

Introductory Chapter: Development of Data Clustering

Tang¹,

Wu²

2022

Data Clustering

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“…Also, these findings were successfully visualized with an average volume of 749 mm3 for the 3D construction of the tumor site on the left side of the patient's brain. M. Kalra et al [77] validated the developed approach to medical imagery. By adding both synthetic and actual datasets to the algorithm.…”

Section: )mentioning

confidence: 99%

Medical Images Segmentation Based on Unsupervised Algorithms: A Review

Armya

Abdulazeez

2021

QAJ

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Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

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