Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms.
Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting an...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2940 - 2955 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283978&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283978 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283978 182283978 182283978 10.1007/s10278-024-01155-1 182283978 ppf: 2940 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms. aug: au: Carrilero-Mardones, Mikel Parras-Jurado, Manuela Nogales, Alberto Pérez-Martín, Jorge Díez, Francisco Javier affil: https://ror.org/02msb5n36 Department of Artificial Intelligence, Universidad Nacional de Educacion a Distancia (UNED), Madrid, Spain sug: subj: Breast Neoplasms Ultrasonography Breast Neoplasms Diagnosis Breast Neoplasms Classification Radiology Information Systems Neural Networks (Computer) Diagnosis, Computer Assisted Deep Learning Human Female Funding Source Comparative Studies Breast Neoplasms Pathology Algorithms Image Enhancement Sensitivity and Specificity Female ab: Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen's kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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