A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109 · see citing works

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755 · see citing works

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

International evaluation of an AI system for breast cancer screening

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works

Breast imaging reporting and data system (BI-RADS)

Liberman, L., & Menell, J. H. (2002) · Radiologic Clinics of North America · doi:10.1016/s0033-8389(01)00017-3 · Cited by 2,159 · see citing works

The benefits and harms of breast cancer screening: an independent review

Marmot, M. G. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61611-0 · Cited by 1,495 · see citing works

National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium

Lehman, C. D., Arao, R. F., Sprague, B. L., Lee, J. M., Buist, D. S. M., Kerlikowske, K. M., Henderson, L. M., Onega, T., Tosteson, A. N. A., Rauscher, G. H., et al. (2016) · Radiology · doi:10.1148/radiol.2016161174 · Cited by 667 · see citing works

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Yasaka, K., Akai, H., Abe, O., & Kiryu, S. (2018) · Radiology · doi:10.1148/radiol.2017170706 · Cited by 663 · see citing works

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T. P., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2495 · Cited by 635 · see citing works

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016) · Scientific Reports · doi:10.1038/srep27327 · Cited by 291 · see citing works

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

Ibrahim, A., Primakov, S. P., Beuque, M. P. L., Woodruff, H. C., Halilaj, I., Wu, G., Refaee, T., Granzier, R. W. Y., Widaatalla, Y., Hustinx, R., et al. (2020) · Methods · doi:10.1016/j.ymeth.2020.05.022 · Cited by 266 · see citing works

Discrimination of Mine Seismic Events and Blasts Using the Fisher Classifier, Naive Bayesian Classifier and Logistic Regression

Dong, L., Wesseloo, J., Potvin, Y., & Li, X. (2015) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-015-0733-y · Cited by 253 · see citing works

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols

van Velzen, S. G. M., Lessmann, N., Velthuis, B. K., Bank, I. E. M., van den Bongard, D. H. J. G., Leiner, T., de Jong, P. A., Veldhuis, W. B., Correa, A., Terry, J. G., et al. (2020) · Radiology · doi:10.1148/radiol.2020191621 · Cited by 244 · see citing works

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019) · Radiology · doi:10.1148/radiol.2019182908 · Cited by 210 · see citing works

Insights from the Breast Cancer Screening Trials: How Screening Affects the Natural History of Breast Cancer and Implications for Evaluating Service Screening Programs

Tabár, L. K., Yen, A. M., Wu, W. Y.-Y., Chen, S. L., Chiu, S. Y., Fann, J. C., Ku, M. M., Smith, R. A., Duffy, S. W., & Chen, T. H.-H. (2014) · The Breast Journal · doi:10.1111/tbj.12354 · Cited by 197 · see citing works

Microcalcifications in breast cancer: From pathophysiology to diagnosis and prognosis

O’Grady, S., & Morgan, M. P. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.04.006 · Cited by 157 · see citing works

Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms

Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J., & Li, L. (2019) · Computational and Mathematical Methods in Medicine · doi:10.1155/2019/2717454 · Cited by 141 · see citing works

Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and Le Gal's classification

Gülsün, M., Demirkazık, F., & Ariyurek, M. O. (2003) · European Journal of Radiology · doi:10.1016/s0720-048x(02)00181-x · Cited by 85 · see citing works

Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications

Lei, C., Wei, W., Liu, Z., Xiong, Q., Yang, C., Yang, M., Zhang, L., Zhu, T., Zhuang, X., Liu, C., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108711 · Cited by 45 · see citing works

Comparison of Independent Double Readings and Computer-Aided Diagnosis (CAD) for the Diagnosis of Breast Calcifications

Jiang, Y., Metz, C. E., Nishikawa, R. M., & Schmidt, R. A. (2006) · Academic Radiology · doi:10.1016/j.acra.2005.09.086 · Cited by 32 · see citing works

Lesion size is a major determinant of the mammographic features of ductal carcinoma in situ: findings from the Sloane project

Evans, A. J., Clements, K., Maxwell, A., Bishop, H., Hanby, A. M., Lawrence, G., & Pinder, S. E. (2010) · Clinical Radiology · doi:10.1016/j.crad.2009.05.017 · Cited by 25 · see citing works

Comparison of Positive Predictive Values of Categorization of Suspicious Calcifications Using the 4th and 5th Editions of BI-RADS

Park, G. E., Kim, S. H., Lee, J. M., Kang, B. J., & Chae, B. J. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20866 · Cited by 21 · see citing works

A novel nonparametric confidence interval for differences of proportions for correlated binary data

Duan, C., Cao, Y., Zhou, L., Tan, M. T., & Chen, P. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216679040 · Cited by 5 · see citing works

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100148 · Cited by 201 · see citing works

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Zhang, J., Wu, J., Zhou, X. S., Shi, F., & Shen, D. G. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.001 · Cited by 145 · see citing works

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. J. (2023) · Diagnostics · doi:10.3390/diagnostics13122041 · Cited by 94 · see citing works

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

Tsai, K.-J., Chou, M., Li, H.-M., Liu, S.-T., Hsu, J.-H., Yeh, W.-C., Hung, C., Yeh, C., & Hwang, S. (2022) · Sensors · doi:10.3390/s22031160 · Cited by 58 · see citing works

Deep learning performance for detection and classification of microcalcifications on mammography

Pesapane, F., Trentin, C., Ferrari, F., Signorelli, G., Tantrige, P. M., Montesano, M., Cicala, C., Virgoli, R., D'Acquisto, S., Nicosia, L., et al. (2023) · European Radiology Experimental · doi:10.1186/s41747-023-00384-3 · Cited by 48 · see citing works

Artificial intelligence in breast imaging: Current situation and clinical challenges

You, C., Shen, Y., Sun, S., Zhou, J., Li, J., Su, G.-H., Michalopoulou, E., Peng, W., Gu, Y., Guo, W., et al. (2023) · Exploration · doi:10.1002/exp.20230007 · Cited by 45 · see citing works

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Qasrawi, R., Daraghmeh, O., Qdaih, I., Thwib, S., Polo, S. V., Owienah, H., Al-Halawa, D. A., & Atari, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38374 · Cited by 41 · see citing works

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography

Do, Y. A., Jang, M., Yun, B. L., Shin, S. U., Kim, B., & Kim, S. M. (2021) · Diagnostics · doi:10.3390/diagnostics11081409 · Cited by 35 · see citing works

Deep learning in breast imaging

Bhowmik, A., & Eskreis‐Winkler, S. (2022) · BJR|Open · doi:10.1259/bjro.20210060 · Cited by 36 · see citing works

Classification of asymmetry in mammography via the DenseNet convolutional neural network

Liao, T., Li, L., Ouyang, R., Lin, X. S., Lai, X., Cheng, G., & Ma, J. (2023) · European Journal of Radiology Open · doi:10.1016/j.ejro.2023.100502 · Cited by 33 · see citing works

Breast Cancer Detection Using Mammography: Image Processing to Deep Learning

Qureshi, S. A., Rehman, A. U., Hussain, L., Sadiq, T., Shah, S. T. H., Mir, A. A., Nadim, M. A., Williams, D. K. A., Duong, T. Q., Chaudhary, Q.-U.-A., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3523745 · Cited by 31 · see citing works

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions

Yin, H., Jiang, Y., Xu, Z., Jia, H., & Lin, G. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04142-7 · Cited by 26 · see citing works

Artificial intelligence in mammography: a systematic review of the external validation

Branco, P. E. S. C., Franco, A. H. S., de Oliveira, A. P., Carneiro, I. M. C., de Carvalho, L. S., de Souza, J. I. N., Leandro, D. R., & Cândido, E. B. (2024) · Revista Brasileira Ginecologia e Obstetrícia · doi:10.61622/rbgo/2024rbgo71 · Cited by 25 · see citing works

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis

Elahi, R., & Nazari, M. (2024) · Radiological Physics and Technology · doi:10.1007/s12194-024-00842-6 · Cited by 27 · see citing works

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study

Yang, Y., Zhong, Y., Li, J., Feng, J., Gong, C., Yu, Y., Hu, Y., Gu, R., Wang, H., Liu, F., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001186 · Cited by 23 · see citing works

A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons

Chen, Y., Wang, L., Luo, R., Wang, S., Wang, H., Gao, F., & Wang, D. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.943415 · Cited by 20 · see citing works

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study

Chen, C., Liu, Y., Yao, J., Wang, K., Zhang, M., Shi, F., Tian, Y., Gao, L., Ying, Y., Pan, Q., et al. (2023) · BMC Cancer · doi:10.1186/s12885-023-11456-3 · Cited by 18 · see citing works

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions

Zhang, R., Wei, W., Li, R., Li, J., Zhou, Z., Ma, M., Zhao, R., & Zhao, X. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.733260 · Cited by 17 · see citing works

RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study

Qian, W., Li, Z., Chen, W., Yin, H., Zhang, J., Xu, J., & Hu, C. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00948-6 · Cited by 16 · see citing works

Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations

Durur-Subasi, İ., & Özçelik, Ş. B. (2023) · Eurasian Journal of Medicine · doi:10.5152/eurasianjmed.2023.23360 · Cited by 17 · see citing works

A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives

Amin, A., Acharya, U. D., Prakashini, K., Siddalingaswamy, P. C., & Mathew, S. (2025) · BMC Cancer · doi:10.1186/s12885-025-14876-5 · Cited by 13 · see citing works

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

Yang, L., Du, D., Zheng, T., Liu, L., Wang, Z., Du, J., Yi, H., Cui, Y., Liu, D., & Fang, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.948557 · Cited by 12 · see citing works

Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network

Yi, M., Lin, Y., Lin, Z., Xu, Z., Li, L., Huang, R., Huang, W., Wang, N., Zuo, Y., Li, N., et al. (2024) · Clinical Breast Cancer · doi:10.1016/j.clbc.2024.02.003 · Cited by 12 · see citing works

Stereotactic breast biopsy

Chakrabarthi, S. (2021) · The Indian Journal of Medical Research · doi:10.4103/ijmr.ijmr_1815_20 · Cited by 11 · see citing works

What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education

Hunt, V. M., Sprehe, L. K., de Lomba, W. C., Gameiro, R. R., Woite, N. L., Wade, C. G., Chen, J. H., Rougas, S. C., Fraser, H. S. F., Sullivan, A. M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02761-9 · Cited by 10 · see citing works

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review

D'Amiano, A. J., Cheunkarndee, T., Azoba, C. C., Chen, K. Y., Mak, R. H., & Perni, S. (2025) · Cancer Medicine · doi:10.1002/cam4.70728 · Cited by 11 · see citing works

Real-time deployment of BI-RADS breast cancer classifier using deep-learning and FPGA techniques

Maria, H. H., Kayalvizhi, R., Malarvizhi, S., Venkatraman, R., Patil, S. S., & Kumar, A. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01335-2 · Cited by 10 · see citing works

AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications

Yoon, J., Lee, H. S., Kim, M. J., Park, V. Y., Kim, E., & Yoon, J. H. (2022) · European Radiology · doi:10.1007/s00330-022-08961-7 · Cited by 8 · see citing works

Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules

Peng, M., Fan, X., Hu, Q., Mei, X., Wang, B., Wu, Z., Hu, H., Tang, L., Hu, X., Yang, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-85209-6 · Cited by 7 · see citing works

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

Li, Y., Cao, L., Yang, T., Chen, L., Huang, M., Lü, Y., Zhou, S., Liu, H., Xia, J., & Wang, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1399296 · Cited by 6 · see citing works

Deep BI-RADS Network for Improved Cancer Detection from Mammograms

Ben-Artzi, G., Daragma, F., & Mahpod, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78104-9_2 · Cited by 6 · see citing works

Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice

Lastrucci, A., Iosca, N., Wandael, Y., Barra, A., Ricci, R., Cucchiari, J. N., Forini, N., Lepri, G., & Giansanti, D. (2025) · Diagnostics · doi:10.3390/diagnostics15172197 · Cited by 7 · see citing works

Explainable Multi-Modal Fusion-Based Federated Learning for Mortality Prediction in Energy-Constrained Healthcare Systems

Bibi, M., Ahmad, R., Rizwan, A., Khan, A. N., Khan, Q. W., & Kim, D.-H. (2026) · IEEE Access · doi:10.1109/access.2025.3650610 · Cited by 7 · see citing works

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Li, W., Song, Y., Qian, X., Zhou, L., Zhu, H., Shen, L., Dai, Y., Dong, F., & Li, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1390342 · Cited by 6 · see citing works

Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications

Mansour, S. M., Mokhtar, O., Galil, M.-A. S. M. A. E., Taha, S. N., & Shetat, O. M. M. (2024) · British Journal of Radiology · doi:10.1093/bjr/tqae220 · Cited by 6 · see citing works

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography

Cao, K., Gao, F., Long, R., Zhang, F.-D., Huang, C., Cao, M., Yu, Y., & Sun, Y. (2024) · Journal of X-Ray Science and Technology · doi:10.3233/xst-230332 · Cited by 6 · see citing works

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images

Du, Y., Wang, D., Liu, M., Zhang, X., Ren, W., Sun, J., Yin, C. N., Yang, S., & Zhang, L. (2024) · Journal of Cancer Research and Therapeutics · doi:10.4103/jcrt.jcrt_1796_23 · Cited by 6 · see citing works

Multiparametric Ultrasound Breast Tumors Diagnosis Within BI-RADS Category 4 via Feature Disentanglement and Cross-Fusion

Ruan, Z., Song, C., Xu, P., Wang, C., Zhao, J., Chen, M., Li, S., Su, Q., Zhuo, X., Wu, Y., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3558786 · Cited by 5 · see citing works

Residual shallow convolutional neural network to classify microcalcifications clusters in digital mammograms

Lozoya, R. S. L., Domínguez, H. D. J. O., Sossa, H., Sánchez, V. G. C., & Villegas, O. O. V. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107209 · Cited by 5 · see citing works

Transfer Learning Based Breast Cancer Detection and Classification using Mammogram Images

Remya, R., & Rajini, N. H. (2022) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears53579.2022.9751974 · Cited by 5 · see citing works

An integrated model for early breast cancer prediction using microcalcifications and patient risk factors

Sreelekshmi, V., K, P., & Nair, J. J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00775-y · Cited by 4 · see citing works

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

Yang, Y., Liao, T., Lin, X. S., Ouyang, R., Cao, Z., Hu, J., & Ma, J. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1543553 · Cited by 4 · see citing works

Identifying clinically relevant findings in breast cancer using deep learning and feature attribution on local views from high-resolution mammography

Mellado, D., Mayeta-Revilla, L., Sotelo, J. A., Querales, M., Godoy, E., Lever, S., Pardo, F., Chabert, S., & Rodrigo Salas, F. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1601929 · Cited by 4 · see citing works

Breast Cancer Diagnosis with XAI-Integrated Deep Learning Approach

Tanim, S. A., Alam, G. M. I., Shrestha, T. E., Islam, M., Jahan, F., & Nur, K. M. (2024) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict64318.2024.10824574 · Cited by 4 · see citing works

A Mammography-Based Radiomic Nomogram for Predicting Malignancy in Breast Suspicious Microcalcifications

Chen, Y., Jiang, H., Li, J., Zhang, J., Wu, P., & Dai, Z. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.09.033 · Cited by 4 · see citing works

Deep Neural Network-Based Model for Breast Cancer Lesion Diagnosis in Mammography Images

Yakoubi, M. A., Khiari, N., Khiari, A., & Melouah, A. (2024) · Acta Informatica Pragensia · doi:10.18267/j.aip.245 · Cited by 3 · see citing works

Diagnostic performance of ultrasound S-Detect technology in evaluating BI-RADS-4 breast nodules ≤ 20 mm and > 20 mm

Xing, B., Gu, C., Fu, C., Zhang, B., & Tan, Y. (2025) · BMC Cancer · doi:10.1186/s12885-025-14760-2 · Cited by 3 · see citing works

Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning

Liu, F., Li, G., & Wang, J. (2024) · Analytical Methods · doi:10.1039/d4ay01755b · Cited by 3 · see citing works

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T1 and T2 Relaxation Times with Application to Cancer Cell Culture

Truszkiewicz, A., Bartusik‐Aebisher, D., Wojtas, Ł., Cieślar, G., Kawczyk‐Krupka, A., & Aebisher, D. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021554 · Cited by 3 · see citing works

Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

Oladimeji, O. O., Imran, A.-A.-Z., Wang, X., & Unnikrishnan, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105601 · Cited by 4 · see citing works

Deep learning model for the diagnosis of breast cancers smaller than 1 cm with ultrasonography: integration of ultrasonography and clinical factors

Bong, J. H., Kim, T. H., & Jeong, S. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-880 · Cited by 3 · see citing works

Machine Learning Based on Digital Mammography to Reduce the Need for Invasive Biopsies of Benign Calcifications Classified in BI-RADS Category 4

Wang, N., Xu, W., Wang, H., Wu, S., Wang, J., Ao, W., Zhang, C., Zhu, Y., Xie, Z., & Mao, G. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01347-9 · Cited by 2 · see citing works

A deep learning system for non-invasive breast cancer diagnosis with multimodal data

Li, Y., Zhang, J., Chen, H., Yang, L., Xie, Y., Xu, Q., Li, P., Li, J., Li, Z., Dai, L., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01654-2 · Cited by 4 · see citing works

A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data

Lu, M., Li, Z., & Gao, J. (2025) · Journal of X-Ray Science and Technology · doi:10.1177/08953996241308175 · Cited by 2 · see citing works

Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset

Lozoya, R. S. L., Domínguez, H. D. J. O., Sossa, H., Sánchez, V. G. C., Villegas, O. O. V., & Barragán, K. N. (2025) · Mathematics · doi:10.3390/math13152422 · Cited by 2 · see citing works

Examining the role of AI in cancer imaging through the lens of clinical studies

Marcu, L. G., & Marcu, D. C. (2025) · Health and Technology · doi:10.1007/s12553-025-01019-w · Cited by 3 · see citing works

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Curia, F. (2022) · Journal of Quality in Health Care & Economics · doi:10.23880/jqhe-16000310 · Cited by 2 · see citing works

Prediction of metastatic risk of renal clear cell carcinoma based on CT radiomics analysis

Wang, X., Yang, Y., Wu, J., Tang, X., & Wang, Y. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1576956 · Cited by 1 · see citing works

MACHINE LEARNING-BASED MICROCALCIFICATION DETECTION FROM MAMMOGRAMS: FORMER TECHNIQUES AND CHALLENGES, FUTURE RESEARCH TRENDS

Anitha, D. J., & Malathi, S. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225300032 · Cited by 1 · see citing works

Suporte à decisão clínica baseado em IA: revisão de escopo

Correia, E. A. S., & Netto, A. V. (2025) · Journal of Health Informatics · doi:10.59681/2175-4411.v17.2025.1457 · Cited by 2 · see citing works

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Mansour, S. M., El-Sharawy, A. A., Abdelghaffar, S. M., Abdel-Salam, G. M., & Elsheikh, R. (2026) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-026-01682-7 · Cited by 1 · see citing works

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Peulić, A., Jovanović, Ž., & Milošević, M. (2026) · Technology and Health Care · doi:10.1177/09287329261442665 · Cited by 1 · see citing works

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Bothou, Α., Bolou, A., Dinas, K. D., Kyrkou, G. A., Hardy, D., Pappou, P., Varela, P., Margioula‐Siarkou, G., Balafouta, M., & Diamanti, A. (2026) · Healthcare · doi:10.3390/healthcare14121674 · Cited by 1 · see citing works

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Zhang, J., Pfob, A., Reisig, E., & Cai, L. (2026) · Diagnostics · doi:10.3390/diagnostics16121839 · Cited by 1 · see citing works

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Lee, M., Lee, S. E., Kim, H. R., Kim, M. J., Yoon, J. H., & Park, V. Y. (2022) · European Radiology · doi:10.1007/s00330-022-08596-8 · Cited by 1 · see citing works

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Pullaiah, N. R. P., Venkatasekhar, D., & Venkatramana, P. (2022) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs56523.2022.10060790 · Cited by 1 · see citing works

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Ugwueke, E. C., & Palogiannidis, D. (2023) · IEEE International Conference on Soft Computing and Measurements (SCM) · doi:10.1109/scm58628.2023.10159092 · Cited by 1 · see citing works

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Yong, E., Teo, Y. N., McKnoulty, L., Gautam, A., Chaulagain, R., & Yong, K. H. (2024) · Journal of Chitwan Medical College · doi:10.54530/jcmc.1474 · Cited by 1 · see citing works

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Dif, N., Boudinar, M. E. A., Abdelali, M. A., Asma, B. J., & Benslimane, S. M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20001-8 · Cited by 1 · see citing works

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Oladimeji, O. O., Ayaz, H., McLoughlin, I., & Unnikrishnan, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77789-9_10 · Cited by 1 · see citing works

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(2022) · International journal of intelligent engineering and systems · doi:10.22266/ijies2022.1231.35 · Cited by 1 · see citing works

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Li, H., & Xie, T. (2023) · Archives of Medical Science · doi:10.5114/aoms/168973

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邓, 贵. (2024) · World Journal of Cancer Research · doi:10.12677/wjcr.2024.141004

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Amin, A., Acharya, U. D., Siddalingaswamy, P. C., Prakashini, K., & Mathew, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4181414/v1

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Dong, M., Zhang, B., Fu, C., Xu, X., & Xing, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4223674/v1

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Pontes, M. R., Damasceno, M. N., da Silva, G. L., & Mariquito, M. F. A. (2024) · Editora Pascal LTDA eBooks · doi:10.29327/5424868

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Dif, N., Abdelali, M. A., Boudinar, M. E. A., Asma, B. J., & Benslimane, S. M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71848-9_7

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Anitha, D. J., & Malathi, S. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859823

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Wang, C., Li, C., & Lin, G. (2025) · Journal of Imaging · doi:10.3390/jimaging11070240

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Pesapane, F., Trentin, C., Ferrari, F., Signorelli, G., Tantrige, P. M., Montesano, M., Cicala, C., Virgoli, R., D'Acquisto, S., Nicosia, L., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26636916

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Lu, W., Tang, Y., Luo, J., Zhang, C., & Zhu, L. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102260

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Zhang, Y., Fan, Y., Yao, X., Zhou, H., Han, D. H., Xiao, Y., Zhong, P., & Fang, J. (2026) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s604929

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Unal, S., & Açıkgözoğlu, E. (2026) · Tomography · doi:10.3390/tomography12060088

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Bartusik‐Aebisher, D., Czech, S., Szpara, J., Paul, A., Xavierselvan, M., & Aebisher, D. (2026) · Algorithms · doi:10.3390/a19070524

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Li, W., Kuang, Y., Wang, H., Li, J., Wang, M., 邓明海, Lü, X., Shang, Y., & Li, Y. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02129-1

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Ben-Artzi, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31920-3_33

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El-Kholy, A., Abdelrahman, N., & Hassan, K. (2023) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/l892586146

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Zeb, S., & Aman, N. (2025) · Biological and Clinical Sciences Research Journal · doi:10.54112/bcsrj.v6i5.2422

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Dou, Z., Shao, Z., Li, S., Lin, J., Yin, R., Liu, L., Hu, C., Ji, Y., Guo, Y., Wang, P., et al. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02819-w

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Xu, W., Deng, S., Mao, G., Wang, N., Huang, Y., Zhang, C., Sa, G., Wu, S., & An, Y. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.07.062 · Cited by 5 · see citing works

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Wei, J., Li, J., Pan, R., Cao, M., Ma, C., Xie, Z., Ma, Y., & Gao, Z. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02673-w

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Wang, N., Xu, W., Wang, H., Wu, S., Wang, J., Ao, W., Zhang, C., Zhu, Y., Xie, Z., & Mao, G. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01347-9 · Cited by 2 · see citing works

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016) · Scientific Reports · doi:10.1038/srep27327 · Cited by 291 · see citing works