Online variational inference on finite multivariate Beta mixture models for medical applications
Abstract Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-07-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12154 |
| _version_ | 1811266478748467200 |
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| author | Narges Manouchehri Meeta Kalra Nizar Bouguila |
| author_facet | Narges Manouchehri Meeta Kalra Nizar Bouguila |
| author_sort | Narges Manouchehri |
| collection | DOAJ |
| description | Abstract Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria. |
| first_indexed | 2024-04-12T20:42:49Z |
| format | Article |
| id | doaj.art-a879d80c300e4220bb553c964badc41a |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-12T20:42:49Z |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-a879d80c300e4220bb553c964badc41a2022-12-22T03:17:21ZengWileyIET Image Processing1751-96591751-96672021-07-011591869188210.1049/ipr2.12154Online variational inference on finite multivariate Beta mixture models for medical applicationsNarges Manouchehri0Meeta Kalra1Nizar Bouguila2Concordia Institute for Information Systems Engineering Concordia University Montreal Quebec CanadaConcordia Institute for Information Systems Engineering Concordia University Montreal Quebec CanadaConcordia Institute for Information Systems Engineering Concordia University Montreal Quebec CanadaAbstract Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria.https://doi.org/10.1049/ipr2.12154Biomedical measurement and imagingOther topics in statisticsOther topics in statisticsPatient diagnostic methods and instrumentationBiology and medical computing |
| spellingShingle | Narges Manouchehri Meeta Kalra Nizar Bouguila Online variational inference on finite multivariate Beta mixture models for medical applications IET Image Processing Biomedical measurement and imaging Other topics in statistics Other topics in statistics Patient diagnostic methods and instrumentation Biology and medical computing |
| title | Online variational inference on finite multivariate Beta mixture models for medical applications |
| title_full | Online variational inference on finite multivariate Beta mixture models for medical applications |
| title_fullStr | Online variational inference on finite multivariate Beta mixture models for medical applications |
| title_full_unstemmed | Online variational inference on finite multivariate Beta mixture models for medical applications |
| title_short | Online variational inference on finite multivariate Beta mixture models for medical applications |
| title_sort | online variational inference on finite multivariate beta mixture models for medical applications |
| topic | Biomedical measurement and imaging Other topics in statistics Other topics in statistics Patient diagnostic methods and instrumentation Biology and medical computing |
| url | https://doi.org/10.1049/ipr2.12154 |
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