AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning.

Mehta, Pritesh; Antonelli, Michela; Singh, Saurabh; Grondecka, Natalia; Johnston, Edward W; Ahmed, Hashim U; Emberton, Mark; Punwani, Shonit; Ourselin, Sébastien

Mehta, Pritesh; Antonelli, Michela; Singh, Saurabh; Grondecka, Natalia; Johnston, Edward W; Ahmed, Hashim U; Emberton, Mark; Punwani, Shonit; Ourselin, Sébastien.

Afiliación

Mehta P; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Antonelli M; School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH, UK.
Singh S; School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH, UK.
Grondecka N; Centre for Medical Imaging, University College London, London WC1E 6BT, UK.
Johnston EW; Department of Medical Radiology, Medical University of Lublin, 20-059 Lublin, Poland.
Ahmed HU; Interventional Radiology, Royal Marsden Hospital, London SW3 6JJ, UK.
Emberton M; Imperial Prostate, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
Punwani S; Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London WC1E 6BT, UK.
Ourselin S; Centre for Medical Imaging, University College London, London WC1E 6BT, UK.

Cancers (Basel) ; 13(23)2021 Dec 06.

Article en En | MEDLINE | ID: mdl-34885246

Palabras clave

automatic report; computer-aided diagnosis; convolutional neural network; deep learning; lesion classification; lesion detection; magnetic resonance imaging; prostate cancer; segmentation

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Cancers (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

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