These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
141 related articles for article (PubMed ID: 38106316)
1. Clinical application of convolutional neural network for mass analysis on mammograms. Li L; Lin X; Liao T; Ouyang R; Li M; Yuan J; Ma J Quant Imaging Med Surg; 2023 Dec; 13(12):8413-8422. PubMed ID: 38106316 [TBL] [Abstract][Full Text] [Related]
2. A deep learning method for classifying mammographic breast density categories. Mohamed AA; Berg WA; Peng H; Luo Y; Jankowitz RC; Wu S Med Phys; 2018 Jan; 45(1):314-321. PubMed ID: 29159811 [TBL] [Abstract][Full Text] [Related]
3. Classification of asymmetry in mammography via the DenseNet convolutional neural network. Liao T; Li L; Ouyang R; Lin X; Lai X; Cheng G; Ma J Eur J Radiol Open; 2023 Dec; 11():100502. PubMed ID: 37448557 [TBL] [Abstract][Full Text] [Related]
4. Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance. Ma S; Li Y; Yin J; Niu Q; An Z; Du L; Li F; Gu J Front Oncol; 2024; 14():1374278. PubMed ID: 38756651 [TBL] [Abstract][Full Text] [Related]
5. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Liu H; Chen Y; Zhang Y; Wang L; Luo R; Wu H; Wu C; Zhang H; Tan W; Yin H; Wang D Eur Radiol; 2021 Aug; 31(8):5902-5912. PubMed ID: 33496829 [TBL] [Abstract][Full Text] [Related]
6. Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study. He Z; Li Y; Zeng W; Xu W; Liu J; Ma X; Wei J; Zeng H; Xu Z; Wang S; Wen C; Wu J; Feng C; Ma M; Qin G; Lu Y; Chen W Front Oncol; 2021; 11():773389. PubMed ID: 34976817 [TBL] [Abstract][Full Text] [Related]
7. Improved breast lesion detection in mammogram images using a deep neural network. Zhou W; Zhang X; Ding J; Deng L; Cheng G; Wang X Diagn Interv Radiol; 2023 Jul; 29(4):588-595. PubMed ID: 36994940 [TBL] [Abstract][Full Text] [Related]
8. Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study. Gu Y; Xu W; Liu T; An X; Tian J; Ran H; Ren W; Chang C; Yuan J; Kang C; Deng Y; Wang H; Luo B; Guo S; Zhou Q; Xue E; Zhan W; Zhou Q; Li J; Zhou P; Chen M; Gu Y; Chen W; Zhang Y; Li J; Cong L; Zhu L; Wang H; Jiang Y Eur Radiol; 2023 Apr; 33(4):2954-2964. PubMed ID: 36418619 [TBL] [Abstract][Full Text] [Related]
9. Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective. Mohamed AA; Luo Y; Peng H; Jankowitz RC; Wu S J Digit Imaging; 2018 Aug; 31(4):387-392. PubMed ID: 28932980 [TBL] [Abstract][Full Text] [Related]
10. A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography. Tsai KJ; Chou MC; Li HM; Liu ST; Hsu JH; Yeh WC; Hung CM; Yeh CY; Hwang SH Sensors (Basel); 2022 Feb; 22(3):. PubMed ID: 35161903 [TBL] [Abstract][Full Text] [Related]
11. A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network. Qian X; Zhang B; Liu S; Wang Y; Chen X; Liu J; Yang Y; Chen X; Wei Y; Xiao Q; Ma J; Shung KK; Zhou Q; Liu L; Chen Z Eur Radiol; 2020 May; 30(5):3023-3033. PubMed ID: 32006174 [TBL] [Abstract][Full Text] [Related]
12. Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience. Jin Z; Zhu Y; Zhang S; Xie F; Zhang M; Zhang Y; Tian X; Zhang J; Luo Y; Cao J Med Sci Monit; 2020 Jan; 26():e918452. PubMed ID: 31929498 [TBL] [Abstract][Full Text] [Related]
13. Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass. Liu M; He F; Xiao J Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2022 Aug; 47(8):1089-1098. PubMed ID: 36097777 [TBL] [Abstract][Full Text] [Related]
14. Principal component regression-based contrast-enhanced ultrasound evaluation system for the management of BI-RADS US 4A breast masses: objective assistance for radiologists. Lin ZM; Chen JF; Xu FT; Liu CM; Chen JS; Wang Y; Zhang C; Huang PT Ultrasound Med Biol; 2021 Jul; 47(7):1737-1746. PubMed ID: 33838937 [TBL] [Abstract][Full Text] [Related]
15. The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis. Chen P; Tong J; Lin T; Wang Y; Yu Y; Chen M; Yang G Gland Surg; 2022 Dec; 11(12):1946-1960. PubMed ID: 36654955 [TBL] [Abstract][Full Text] [Related]
16. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Lehman CD; Yala A; Schuster T; Dontchos B; Bahl M; Swanson K; Barzilay R Radiology; 2019 Jan; 290(1):52-58. PubMed ID: 30325282 [TBL] [Abstract][Full Text] [Related]
17. Downgrading and Upgrading Gray-Scale Ultrasound BI-RADS Categories of Benign and Malignant Masses With Optoacoustics: A Pilot Study. Neuschler EI; Lavin PT; Tucker FL; Barke LD; Bertrand ML; Böhm-Vélez M; Destounis S; Dogan BE; Grobmyer SR; Katzen J; Kist KA; Makariou EV; Parris TM; Young CA; Butler R AJR Am J Roentgenol; 2018 Sep; 211(3):689-700. PubMed ID: 29975115 [TBL] [Abstract][Full Text] [Related]
18. Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study. Yu TF; He W; Gan CG; Zhao MC; Zhu Q; Zhang W; Wang H; Luo YK; Nie F; Yuan LJ; Wang Y; Guo YL; Yuan JJ; Ruan LT; Wang YC; Zhang RF; Zhang HX; Ning B; Song HM; Zheng S; Li Y; Guang Y Chin Med J (Engl); 2021 Jan; 134(4):415-424. PubMed ID: 33617184 [TBL] [Abstract][Full Text] [Related]
19. Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses. Berg WA; Cosgrove DO; Doré CJ; Schäfer FK; Svensson WE; Hooley RJ; Ohlinger R; Mendelson EB; Balu-Maestro C; Locatelli M; Tourasse C; Cavanaugh BC; Juhan V; Stavros AT; Tardivon A; Gay J; Henry JP; Cohen-Bacrie C; Radiology; 2012 Feb; 262(2):435-49. PubMed ID: 22282182 [TBL] [Abstract][Full Text] [Related]