234 related articles for article (PubMed ID: 32755754)
1. A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.
Pérez-Benito FJ; Signol F; Perez-Cortes JC; Fuster-Baggetto A; Pollan M; Pérez-Gómez B; Salas-Trejo D; Casals M; Martínez I; LLobet R
Comput Methods Programs Biomed; 2020 Oct; 195():105668. PubMed ID: 32755754
[TBL] [Abstract][Full Text] [Related]
2. Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach.
Larroza A; Pérez-Benito FJ; Perez-Cortes JC; Román M; Pollán M; Pérez-Gómez B; Salas-Trejo D; Casals M; Llobet R
Diagnostics (Basel); 2022 Jul; 12(8):. PubMed ID: 36010173
[TBL] [Abstract][Full Text] [Related]
3. A deep learning framework to classify breast density with noisy labels regularization.
Lopez-Almazan H; Javier Pérez-Benito F; Larroza A; Perez-Cortes JC; Pollan M; Perez-Gomez B; Salas Trejo D; Casals M; Llobet R
Comput Methods Programs Biomed; 2022 Jun; 221():106885. PubMed ID: 35594581
[TBL] [Abstract][Full Text] [Related]
4. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.
Ma X; Wei J; Zhou C; Helvie MA; Chan HP; Hadjiiski LM; Lu Y
Med Phys; 2019 May; 46(5):2103-2114. PubMed ID: 30771257
[TBL] [Abstract][Full Text] [Related]
5. Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.
Gudhe NR; Behravan H; Sudah M; Okuma H; Vanninen R; Kosma VM; Mannermaa A
Sci Rep; 2022 Jul; 12(1):12060. PubMed ID: 35835933
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.
Verburg E; Wolterink JM; de Waard SN; Išgum I; van Gils CH; Veldhuis WB; Gilhuijs KGA
Med Phys; 2019 Oct; 46(10):4405-4416. PubMed ID: 31274194
[TBL] [Abstract][Full Text] [Related]
8. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
[TBL] [Abstract][Full Text] [Related]
9. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
[TBL] [Abstract][Full Text] [Related]
10. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.
Al-Antari MA; Al-Masni MA; Kim TS
Adv Exp Med Biol; 2020; 1213():59-72. PubMed ID: 32030663
[TBL] [Abstract][Full Text] [Related]
11. Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
Pérez-Benito FJ; Signol F; Pérez-Cortés JC; Pollán M; Pérez-Gómez B; Salas-Trejo D; Casals M; Martínez I; LLobet R
Comput Methods Programs Biomed; 2019 Aug; 177():123-132. PubMed ID: 31319940
[TBL] [Abstract][Full Text] [Related]
12. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.
Li S; Wei J; Chan HP; Helvie MA; Roubidoux MA; Lu Y; Zhou C; Hadjiiski LM; Samala RK
Phys Med Biol; 2018 Jan; 63(2):025005. PubMed ID: 29210358
[TBL] [Abstract][Full Text] [Related]
13. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
[TBL] [Abstract][Full Text] [Related]
14. Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.
Rampun A; López-Linares K; Morrow PJ; Scotney BW; Wang H; Ocaña IG; Maclair G; Zwiggelaar R; González Ballester MA; Macía I
Med Image Anal; 2019 Oct; 57():1-17. PubMed ID: 31254729
[TBL] [Abstract][Full Text] [Related]
15. Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms.
Yamamuro M; Asai Y; Hashimoto N; Yasuda N; Kimura H; Yamada T; Nemoto M; Kimura Y; Handa H; Yoshida H; Abe K; Tada M; Habe H; Nagaoka T; Nin S; Ishii K; Kondo Y
Biomed Phys Eng Express; 2022 Jun; 8(4):. PubMed ID: 35728581
[TBL] [Abstract][Full Text] [Related]
16. Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.
Squires S; Harkness EF; Mackenzie A; Evans DG; Howell SJ; Astley SM
Biomed Phys Eng Express; 2024 May; 10(4):. PubMed ID: 38701765
[No Abstract] [Full Text] [Related]
17. A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.
Yin K; Yan S; Song C; Zheng B
Int J Comput Assist Radiol Surg; 2019 Feb; 14(2):237-248. PubMed ID: 30288698
[TBL] [Abstract][Full Text] [Related]
18. Fully Automated Breast Density Segmentation and Classification Using Deep Learning.
Saffari N; Rashwan HA; Abdel-Nasser M; Kumar Singh V; Arenas M; Mangina E; Herrera B; Puig D
Diagnostics (Basel); 2020 Nov; 10(11):. PubMed ID: 33238512
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
