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Implication and Applications of Mach...

Hagerty, Jason Richard,

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Implication and Applications of Machine Learning on Biomedical Images /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Implication and Applications of Machine Learning on Biomedical Images // Jason Richard Hagerty.
Author:	Hagerty, Jason Richard,
Description:	1 electronic resource (90 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Computer engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997076
ISBN:	9798286424283

Implication and Applications of Machine Learning on Biomedical Images /
Hagerty, Jason Richard,

Implication and Applications of Machine Learning on Biomedical Images /Jason Richard Hagerty. - 1 electronic resource (90 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Medical imaging ranges in modality including computer tomography imaging, x-ray imaging, digital microscopy, and macro-focus dermoscopy images. The latter two modalities are the focus of the presented work. To perform a diagnostic evaluation on the captured dermoscopy image, it begins with what is usually a labor-intensive operation that requires an expert to perform the initial segmentation for localizing a region of interest (ROI). Once that ROI is obtained, a physician with years of training and experience will observe biological markers that can be used to visually differentiate whether a lesion is benign or malignant. A similar process is used for digital microscopy images. Once a sample has been prepared by slicing and staining a specimen, the ROI of the captured image is also localized and again a physician then judges the subtle difference in the ROI to deduce a diagnostic score. All this relies on having image data to use for machine learning as well as creating image-processing algorithms. Research is a way to infer how much data is needed given the current state-of-the-art methodologies. Data collection is hampered by the fact that, traditionally, medical data is treated differently than other forms of images and metadata in that a patient is entitled to privacy thus making assembling a large medical dataset difficult as well as limiting its release via open source. The presented work demonstrates methods that machine learning and image processing researchers could leverage to overcome the previously mentioned difficulties with the goal being to aid the physician in an effort to increase the number of cases that can be screened and/or evaluated.

English

ISBN: 9798286424283Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Machine learning

Implication and Applications of Machine Learning on Biomedical Images /
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520 $a Medical imaging ranges in modality including computer tomography imaging, x-ray imaging, digital microscopy, and macro-focus dermoscopy images. The latter two modalities are the focus of the presented work. To perform a diagnostic evaluation on the captured dermoscopy image, it begins with what is usually a labor-intensive operation that requires an expert to perform the initial segmentation for localizing a region of interest (ROI). Once that ROI is obtained, a physician with years of training and experience will observe biological markers that can be used to visually differentiate whether a lesion is benign or malignant. A similar process is used for digital microscopy images. Once a sample has been prepared by slicing and staining a specimen, the ROI of the captured image is also localized and again a physician then judges the subtle difference in the ROI to deduce a diagnostic score. All this relies on having image data to use for machine learning as well as creating image-processing algorithms. Research is a way to infer how much data is needed given the current state-of-the-art methodologies. Data collection is hampered by the fact that, traditionally, medical data is treated differently than other forms of images and metadata in that a patient is entitled to privacy thus making assembling a large medical dataset difficult as well as limiting its release via open source. The presented work demonstrates methods that machine learning and image processing researchers could leverage to overcome the previously mentioned difficulties with the goal being to aid the physician in an effort to increase the number of cases that can be screened and/or evaluated.
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