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Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.
Vigil, Nicolle; Barry, Madeline; Amini, Arya; Akhloufi, Moulay; Maldague, Xavier P V; Ma, Lan; Ren, Lei; Yousefi, Bardia.
Afiliación
  • Vigil N; Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
  • Barry M; Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
  • Amini A; Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA.
  • Akhloufi M; Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada.
  • Maldague XPV; Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.
  • Ma L; Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
  • Ren L; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
  • Yousefi B; Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
Cancers (Basel) ; 14(11)2022 May 27.
Article en En | MEDLINE | ID: mdl-35681643
Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images-437 benign, 210, malignant, and 133 normal-were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1-84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cancers (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
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Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cancers (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza