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Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Ourresults, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.
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Über die Autorin bzw. den Autor
Prof.Gajanan Kale is Presently working as Assistant Professor in CSE Dept. at FTC COER,Sangola and he is having more than 10 years of Academic experience and handling various subjects for UG-PG level. Dr.Somnath Thigale is Head & Associate Professor at CSE Dept. FTC COER,Sangla & having 17+ years of experience.
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An Optimal Multilevel Thresholding Based for Color Image Segmentation
Verlag:
LAP LAMBERT Academic Publishing Aug 2023, 2023
ISBN 10: 6206767590
ISBN 13: 9786206767596
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Ourresults, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions. 76 pp. Englisch. Bestandsnummer des Verkäufers 9786206767596
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An Optimal Multilevel Thresholding Based for Color Image Segmentation
Verlag:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206767590
ISBN 13: 9786206767596
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Ourresults, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions. Bestandsnummer des Verkäufers 9786206767596
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An Optimal Multilevel Thresholding Based for Color Image Segmentation
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LAP Lambert Academic Publishing, 2023
ISBN 10: 6206767590
ISBN 13: 9786206767596
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable inv. Bestandsnummer des Verkäufers 1104277288
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An Optimal Multilevel Thresholding Based for Color Image Segmentation
Verlag:
LAP LAMBERT Academic Publishing Aug 2023, 2023
ISBN 10: 6206767590
ISBN 13: 9786206767596
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Ourresults, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Bestandsnummer des Verkäufers 9786206767596
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An Optimal Multilevel Thresholding Based for Color Image Segmentation
Verlag:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206767590
ISBN 13: 9786206767596
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Taschenbuch. Zustand: Neu. An Optimal Multilevel Thresholding Based for Color Image Segmentation | Gajanan Kale (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206767596 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 127637337
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