225 related articles for article (PubMed ID: 30309858)
1. Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.
Aboutalib SS; Mohamed AA; Berg WA; Zuley ML; Sumkin JH; Wu S
Clin Cancer Res; 2018 Dec; 24(23):5902-5909. PubMed ID: 30309858
[TBL] [Abstract][Full Text] [Related]
2. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.
Kooi T; van Ginneken B; Karssemeijer N; den Heeten A
Med Phys; 2017 Mar; 44(3):1017-1027. PubMed ID: 28094850
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Deep Learning to Improve Breast Cancer Detection on Screening Mammography.
Shen L; Margolies LR; Rothstein JH; Fluder E; McBride R; Sieh W
Sci Rep; 2019 Aug; 9(1):12495. PubMed ID: 31467326
[TBL] [Abstract][Full Text] [Related]
5. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.
Mendel K; Li H; Sheth D; Giger M
Acad Radiol; 2019 Jun; 26(6):735-743. PubMed ID: 30076083
[TBL] [Abstract][Full Text] [Related]
6. Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.
Clancy K; Aboutalib S; Mohamed A; Sumkin J; Wu S
J Digit Imaging; 2020 Oct; 33(5):1257-1265. PubMed ID: 32607908
[TBL] [Abstract][Full Text] [Related]
7. Deep Convolutional Neural Networks for breast cancer screening.
Chougrad H; Zouaki H; Alheyane O
Comput Methods Programs Biomed; 2018 Apr; 157():19-30. PubMed ID: 29477427
[TBL] [Abstract][Full Text] [Related]
8. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.
Li X; Qin G; He Q; Sun L; Zeng H; He Z; Chen W; Zhen X; Zhou L
Eur Radiol; 2020 Feb; 30(2):778-788. PubMed ID: 31691121
[TBL] [Abstract][Full Text] [Related]
9. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.
Gao F; Wu T; Li J; Zheng B; Ruan L; Shang D; Patel B
Comput Med Imaging Graph; 2018 Dec; 70():53-62. PubMed ID: 30292910
[TBL] [Abstract][Full Text] [Related]
10. Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.
Sawyer Lee R; Dunnmon JA; He A; Tang S; Ré C; Rubin DL
J Biomed Inform; 2021 Jan; 113():103656. PubMed ID: 33309994
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Outcomes for One-View Asymmetries Recalled From Digital Breast Tomosynthesis Versus Full-Field Digital Mammography Screening Examinations.
Gong AJ; Nguyen DL; Lee EE; Mullen LA; Myers KS; Oluyemi E; Ambinder EB
AJR Am J Roentgenol; 2022 Nov; 219(5):724-733. PubMed ID: 35703412
[No Abstract] [Full Text] [Related]
12. A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.
Pedemonte S; Tsue T; Mombourquette B; Truong Vu YN; Matthews T; Morales Hoil R; Shah M; Ghare N; Zingman-Daniels N; Holley S; Appleton CM; Su J; Wahl RL
Radiol Artif Intell; 2024 May; 6(3):e230033. PubMed ID: 38597785
[TBL] [Abstract][Full Text] [Related]
13. The Potential Impact of Digital Breast Tomosynthesis on the Benign Biopsy Rate in Women Recalled within the UK Breast Screening Programme.
Sharma N; McMahon M; Haigh I; Chen Y; Dall BJG
Radiology; 2019 May; 291(2):310-317. PubMed ID: 30888932
[TBL] [Abstract][Full Text] [Related]
14. A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis.
Michell MJ; Iqbal A; Wasan RK; Evans DR; Peacock C; Lawinski CP; Douiri A; Wilson R; Whelehan P
Clin Radiol; 2012 Oct; 67(10):976-81. PubMed ID: 22625656
[TBL] [Abstract][Full Text] [Related]
15. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets.
Ueda D; Yamamoto A; Onoda N; Takashima T; Noda S; Kashiwagi S; Morisaki T; Fukumoto S; Shiba M; Morimura M; Shimono T; Kageyama K; Tatekawa H; Murai K; Honjo T; Shimazaki A; Kabata D; Miki Y
PLoS One; 2022; 17(3):e0265751. PubMed ID: 35324962
[TBL] [Abstract][Full Text] [Related]
16. Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance.
Kerschke L; Weigel S; Rodriguez-Ruiz A; Karssemeijer N; Heindel W
Eur Radiol; 2022 Feb; 32(2):842-852. PubMed ID: 34383147
[TBL] [Abstract][Full Text] [Related]
17. External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.
Benndorf M; Burnside ES; Herda C; Langer M; Kotter E
Med Phys; 2015 Aug; 42(8):4987-96. PubMed ID: 26233224
[TBL] [Abstract][Full Text] [Related]
18. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification.
Wang X; Lederman D; Tan J; Wang XH; Zheng B
Acad Radiol; 2010 Oct; 17(10):1234-41. PubMed ID: 20619697
[TBL] [Abstract][Full Text] [Related]
19. Screening outcome in women repeatedly recalled for the same mammographic abnormality before, during and after the transition from screen-film to full-field digital screening mammography.
van Bommel R; Voogd AC; Louwman MW; Strobbe LJ; Venderink D; Duijm LE
Eur Radiol; 2017 Feb; 27(2):553-561. PubMed ID: 27180183
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
