Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem

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Standard

Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. / Tennakoon, Ruwan; Bortsova, Gerda; Orting, Silas; Gostar, Amirali K; Wille, Mathilde M W; Saghir, Zaigham; Hoseinnezhad, Reza; de Bruijne, Marleen; Bab-Hadiashar, Alireza.

I: IEEE Transactions on Medical Imaging, Bind 39, Nr. 4, 2020, s. 854-865.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Tennakoon, R, Bortsova, G, Orting, S, Gostar, AK, Wille, MMW, Saghir, Z, Hoseinnezhad, R, de Bruijne, M & Bab-Hadiashar, A 2020, 'Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem', IEEE Transactions on Medical Imaging, bind 39, nr. 4, s. 854-865. https://doi.org/10.1109/TMI.2019.2936244

APA

Tennakoon, R., Bortsova, G., Orting, S., Gostar, A. K., Wille, M. M. W., Saghir, Z., Hoseinnezhad, R., de Bruijne, M., & Bab-Hadiashar, A. (2020). Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. IEEE Transactions on Medical Imaging, 39(4), 854-865. https://doi.org/10.1109/TMI.2019.2936244

Vancouver

Tennakoon R, Bortsova G, Orting S, Gostar AK, Wille MMW, Saghir Z o.a. Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. IEEE Transactions on Medical Imaging. 2020;39(4):854-865. https://doi.org/10.1109/TMI.2019.2936244

Author

Tennakoon, Ruwan ; Bortsova, Gerda ; Orting, Silas ; Gostar, Amirali K ; Wille, Mathilde M W ; Saghir, Zaigham ; Hoseinnezhad, Reza ; de Bruijne, Marleen ; Bab-Hadiashar, Alireza. / Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. I: IEEE Transactions on Medical Imaging. 2020 ; Bind 39, Nr. 4. s. 854-865.

Bibtex

@article{7cfe07772c7b4ff6aee7a02a5bf6361d,
title = "Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem",
abstract = "Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.",
author = "Ruwan Tennakoon and Gerda Bortsova and Silas Orting and Gostar, {Amirali K} and Wille, {Mathilde M W} and Zaigham Saghir and Reza Hoseinnezhad and {de Bruijne}, Marleen and Alireza Bab-Hadiashar",
year = "2020",
doi = "10.1109/TMI.2019.2936244",
language = "English",
volume = "39",
pages = "854--865",
journal = "I E E E Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS

TY - JOUR

T1 - Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem

AU - Tennakoon, Ruwan

AU - Bortsova, Gerda

AU - Orting, Silas

AU - Gostar, Amirali K

AU - Wille, Mathilde M W

AU - Saghir, Zaigham

AU - Hoseinnezhad, Reza

AU - de Bruijne, Marleen

AU - Bab-Hadiashar, Alireza

PY - 2020

Y1 - 2020

N2 - Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.

AB - Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.

U2 - 10.1109/TMI.2019.2936244

DO - 10.1109/TMI.2019.2936244

M3 - Journal article

C2 - 31425069

VL - 39

SP - 854

EP - 865

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

SN - 0278-0062

IS - 4

ER -

ID: 227842826