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Advancements in Oncology with Artificial Intelligence—A Review Article
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In: Cancers; Volume 14; Issue 5; Pages: 1349 (2022)
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Management and Outcome of Young Women (≤40 Years) with Breast Cancer in Switzerland
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In: Cancers; Volume 14; Issue 5; Pages: 1328 (2022)
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Metaphors of cancer in the Arabic language: An analysis of the use of metaphors in the online narratives of breast cancer patients
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In: Open Linguistics, Vol 8, Iss 1, Pp 27-45 (2022) (2022)
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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| 8 |
Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound
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In: Diagnostics; Volume 12; Issue 1; Pages: 66 (2021)
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Abstract:
In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.
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Keyword:
breast cancer; deep convolutional network; image classification; semantic segmentation; ultrasonic imaging
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URL: https://doi.org/10.3390/diagnostics12010066
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| 11 |
Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results
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In: Cancers; Volume 13; Issue 24; Pages: 6273 (2021)
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| 12 |
Culture and breast cancer surgical decisions and experiences
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| 13 |
Characterization of circulating breast cancer cells with tumorigenic and metastatic capacity
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In: ISSN: 1757-4676 ; EISSN: 1757-4684 ; EMBO Molecular Medicine ; https://hal.archives-ouvertes.fr/hal-03602489 ; EMBO Molecular Medicine, Wiley Open Access, 2020, 12 (9), pp.e11908. ⟨10.15252/emmm.201911908⟩ (2020)
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| 14 |
Breast cancer patients' language use across four stages ...
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| 15 |
Pattern Recognition of Non-Mass Enhancements on Breast MRI – A Pictorial Review with Radiologic-Pathologic Correlation. ...
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Role of BD4BREAST in supporting the categorization of mammographic findings according to the BI-RADS mammographic lexicon ...
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Can MRI Biomarkers Predict Triple-Negative Breast Cancer?
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In: Diagnostics; Volume 10; Issue 12; Pages: 1090 (2020)
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| 18 |
Exploring Intimacy in Collaborative Photographic Narratives of Breast Cancer
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In: Humanities ; Volume 9 ; Issue 1 (2020)
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| 19 |
Using artificial intelligence to analyse and teach communication in healthcare
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In: Breast (2020)
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| 20 |
Genetic testing and eHealth usage among Deaf women.
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In: Journal of genetic counseling, vol 28, iss 5 (2019)
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