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Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.

• Authors : Yang L; Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.; Zhang N

Subjects: Deep Learning* ; Breast Neoplasms*/Breast Neoplasms*/Breast Neoplasms*/diagnostic imaging; Humans

• Source: Scientific reports [Sci Rep] 2024 Dec 28; Vol. 14 (1), pp. 31479. Date of Electronic Publication: 2024 Dec 28.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.

• Authors : Hussain S; School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, 64849, Nuevo Leon, Mexico. .; Naseem U

Subjects: Natural Language Processing* ; Deep Learning* ; Machine Learning*

• Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Oct 24; Vol. 24 (1), pp. 310. Date of Electronic Publication: 2024 Oct 24.Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947

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Two-stage augmentation for detecting malignancy of BI-RADS 3 lesions in early breast cancer.

• Authors : Tian, Huanhuan¹ (AUTHOR); Cai, Li¹ (AUTHOR); Gui, Yu² (AUTHOR)

• Source: BMC Cancer. 4/17/2025, Vol. 25 Issue 1, p1-13. 13p.

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Reducing unnecessary biopsies of BI-RADS 4 lesions based on a deep learning model for mammography

• Authors : Yuting Yang; Tingting Liao; Xiaohui Lin

Subjects: mammography; artificial intelligence; deep learning

• Source: Frontiers in Oncology, Vol 15 (2025)

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Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks.

• Authors : Tan, Hongna^1,2 (AUTHOR); Wu, Qingxia^3,4 (AUTHOR); Wu, Yaping^1,2 (AUTHOR)

• Source: Insights into Imaging. 5/21/2025, Vol. 16 Issue 1, p1-12. 12p.

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Predicting Very Early-Stage Breast Cancer in BI-RADS 3 Lesions of Large Population with Deep Learning

• Authors : Congyu Wang; Changzhen Li; Gengxiao Lin

Subjects: breast cancer; ultrasound; BI-RADS 3

• Source: Journal of Imaging, Vol 11, Iss 7, p 240 (2025)

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TR-BI-RADS: a novel dataset for BI-RADS based mammography classification.

• Authors : Ülgü, Mustafa Mahir¹ (AUTHOR); Zalluhoglu, Cemil² (AUTHOR) ; Birinci, Suayip³ (AUTHOR)

• Source: Neural Computing & Applications. Mar2024, Vol. 36 Issue 7, p3699-3709. 11p.

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Two-level content-based mammogram retrieval using the ACR BI-RADS assessment code and learning-driven distance selection.

• Authors : Jouirou, Amira¹ (AUTHOR); Souissi, Ines¹ (AUTHOR); Barhoumi, Walid^1,2 (AUTHOR)

• Source: Journal of Supercomputing. Jul2024, Vol. 80 Issue 11, p15690-15724. 35p.

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Multiview deep learning networks based on automated breast volume scanner images for identifying breast cancer in BI-RADS 4

• Authors : Yini Li; Cao Li; Tao Yang

Subjects: BI-RADS 4; deep learning; breast cancer

• Source: Frontiers in Oncology, Vol 14 (2024)

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Incorporating Tumor Edge Information for Fine-Grained BI-RADS Classification of Breast Ultrasound Images

• Authors : Meng Xu; Jianhua Huang; Kuan Huang

Subjects: Weakly supervised learning; breast ultrasound; deep learning

• Source: IEEE Access, Vol 12, Pp 38732-38744 (2024)

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