156 related articles for article (PubMed ID: 38074785)
1. External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.
Omoleye OJ; Woodard AE; Howard FM; Zhao F; Yoshimatsu TF; Zheng Y; Pearson AT; Levental M; Aribisala BS; Kulkarni K; Karczmar GS; Olopade OI; Abe H; Huo D
Radiol Artif Intell; 2023 Nov; 5(6):e220299. PubMed ID: 38074785
[TBL] [Abstract][Full Text] [Related]
2. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction.
Donnelly J; Moffett L; Barnett AJ; Trivedi H; Schwartz F; Lo J; Rudin C
Radiology; 2024 Mar; 310(3):e232780. PubMed ID: 38501952
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.
Magni V; Interlenghi M; Cozzi A; Alì M; Salvatore C; Azzena AA; Capra D; Carriero S; Della Pepa G; Fazzini D; Granata G; Monti CB; Muscogiuri G; Pellegrino G; Schiaffino S; Castiglioni I; Papa S; Sardanelli F
Radiol Artif Intell; 2022 Mar; 4(2):e210199. PubMed ID: 35391766
[TBL] [Abstract][Full Text] [Related]
6. A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.
Ezeana CF; He T; Patel TA; Kaklamani V; Elmi M; Brigmon E; Otto PM; Kist KA; Speck H; Wang L; Ensor J; Shih YT; Kim B; Pan IW; Cohen AL; Kelley K; Spak D; Yang WT; Chang JC; Wong STC
Radiol Artif Intell; 2023 Nov; 5(6):e220259. PubMed ID: 38074778
[TBL] [Abstract][Full Text] [Related]
7. Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study.
Santeramo R; Damiani C; Wei J; Montana G; Brentnall AR
Breast Cancer Res; 2024 Feb; 26(1):25. PubMed ID: 38326868
[TBL] [Abstract][Full Text] [Related]
8. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.
Jeffers AM; Sieh W; Lipson JA; Rothstein JH; McGuire V; Whittemore AS; Rubin DL
Radiology; 2017 Feb; 282(2):348-355. PubMed ID: 27598536
[TBL] [Abstract][Full Text] [Related]
9. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.
Dembrower K; Liu Y; Azizpour H; Eklund M; Smith K; Lindholm P; Strand F
Radiology; 2020 Feb; 294(2):265-272. PubMed ID: 31845842
[TBL] [Abstract][Full Text] [Related]
10. Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.
Zhu X; Wolfgruber TK; Leong L; Jensen M; Scott C; Winham S; Sadowski P; Vachon C; Kerlikowske K; Shepherd JA
Radiology; 2021 Dec; 301(3):550-558. PubMed ID: 34491131
[TBL] [Abstract][Full Text] [Related]
11. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.
Yala A; Lehman C; Schuster T; Portnoi T; Barzilay R
Radiology; 2019 Jul; 292(1):60-66. PubMed ID: 31063083
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors.
Michel A; Ro V; McGuinness JE; Mutasa S; Terry MB; Tehranifar P; May B; Ha R; Crew KD
Breast Cancer Res Treat; 2023 Jul; 200(2):237-245. PubMed ID: 37209183
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Automated mammographic breast density estimation using a fully convolutional network.
Lee J; Nishikawa RM
Med Phys; 2018 Mar; 45(3):1178-1190. PubMed ID: 29363774
[TBL] [Abstract][Full Text] [Related]
16. Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms.
Beuque MPL; Lobbes MBI; van Wijk Y; Widaatalla Y; Primakov S; Majer M; Balleyguier C; Woodruff HC; Lambin P
Radiology; 2023 Jun; 307(5):e221843. PubMed ID: 37338353
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study.
Winkel RR; von Euler-Chelpin M; Nielsen M; Petersen K; Lillholm M; Nielsen MB; Lynge E; Uldall WY; Vejborg I
BMC Cancer; 2016 Jul; 16():414. PubMed ID: 27387546
[TBL] [Abstract][Full Text] [Related]
19. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
Hinton B; Ma L; Mahmoudzadeh AP; Malkov S; Fan B; Greenwood H; Joe B; Lee V; Kerlikowske K; Shepherd J
Cancer Imaging; 2019 Jun; 19(1):41. PubMed ID: 31228956
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
