165 related articles for article (PubMed ID: 29430478)
1. Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks.
Antropova N; Abe H; Giger ML
J Med Imaging (Bellingham); 2018 Jan; 5(1):014503. PubMed ID: 29430478
[TBL] [Abstract][Full Text] [Related]
2. Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks.
Antropova N; Huynh B; Li H; Giger ML
J Med Imaging (Bellingham); 2019 Jan; 6(1):011002. PubMed ID: 30840721
[TBL] [Abstract][Full Text] [Related]
3. Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI.
Hu Q; Whitney HM; Li H; Ji Y; Liu P; Giger ML
Radiol Artif Intell; 2021 May; 3(3):e200159. PubMed ID: 34235439
[TBL] [Abstract][Full Text] [Related]
4. U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging.
Douglas L; Bhattacharjee R; Fuhrman J; Drukker K; Hu Q; Edwards A; Sheth D; Giger M
J Med Imaging (Bellingham); 2023 Nov; 10(6):064502. PubMed ID: 37990686
[TBL] [Abstract][Full Text] [Related]
5. Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.
Kapsner LA; Ohlmeyer S; Folle L; Laun FB; Nagel AM; Liebert A; Schreiter H; Beckmann MW; Uder M; Wenkel E; Bickelhaupt S
Eur Radiol; 2022 Sep; 32(9):5997-6007. PubMed ID: 35366123
[TBL] [Abstract][Full Text] [Related]
6. Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging.
Fujioka T; Yashima Y; Oyama J; Mori M; Kubota K; Katsuta L; Kimura K; Yamaga E; Oda G; Nakagawa T; Kitazume Y; Tateishi U
Magn Reson Imaging; 2021 Jan; 75():1-8. PubMed ID: 33045323
[TBL] [Abstract][Full Text] [Related]
7. Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.
Douglas L; Fuhrman J; Hu Q; Edwards A; Sheth D; Abe H; Giger M
J Med Imaging (Bellingham); 2024 May; 11(3):034501. PubMed ID: 38737493
[TBL] [Abstract][Full Text] [Related]
8. A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network.
Zong W; Lee JK; Liu C; Carver EN; Feldman AM; Janic B; Elshaikh MA; Pantelic MV; Hearshen D; Chetty IJ; Movsas B; Wen N
Med Phys; 2020 Sep; 47(9):4077-4086. PubMed ID: 32449176
[TBL] [Abstract][Full Text] [Related]
9. Automated glioma grading on conventional MRI images using deep convolutional neural networks.
Zhuge Y; Ning H; Mathen P; Cheng JY; Krauze AV; Camphausen K; Miller RW
Med Phys; 2020 Jul; 47(7):3044-3053. PubMed ID: 32277478
[TBL] [Abstract][Full Text] [Related]
10. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.
Huynh BQ; Li H; Giger ML
J Med Imaging (Bellingham); 2016 Jul; 3(3):034501. PubMed ID: 27610399
[TBL] [Abstract][Full Text] [Related]
11. Breast DCE-MRI: influence of postcontrast timing on automated lesion kinetics assessments and discrimination of benign and malignant lesions.
Partridge SC; Stone KM; Strigel RM; DeMartini WB; Peacock S; Lehman CD
Acad Radiol; 2014 Sep; 21(9):1195-203. PubMed ID: 24998690
[TBL] [Abstract][Full Text] [Related]
12. Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information.
Jing X; Dorrius MD; Wielema M; Sijens PE; Oudkerk M; van Ooijen P
Cancers (Basel); 2022 Apr; 14(8):. PubMed ID: 35454949
[TBL] [Abstract][Full Text] [Related]
13. 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
Front Oncol; 2022; 12():943415. PubMed ID: 35936673
[TBL] [Abstract][Full Text] [Related]
14. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
[TBL] [Abstract][Full Text] [Related]
15. Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI.
Song L; Lu H; Yin J
PLoS One; 2020; 15(6):e0234800. PubMed ID: 32555662
[TBL] [Abstract][Full Text] [Related]
16. Three-phase dynamic breast magnetic resonance imaging with two-way subtraction.
Choi N; Han BK; Choe YH; Kim HS
J Comput Assist Tomogr; 2005; 29(6):834-41. PubMed ID: 16272861
[TBL] [Abstract][Full Text] [Related]
17. Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT.
Papadomanolakis TN; Sergaki ES; Polydorou AA; Krasoudakis AG; Makris-Tsalikis GN; Polydorou AA; Afentakis NM; Athanasiou SA; Vardiambasis IO; Zervakis ME
Brain Sci; 2023 Feb; 13(2):. PubMed ID: 36831891
[TBL] [Abstract][Full Text] [Related]
18. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.
Hu Q; Whitney HM; Giger ML
Sci Rep; 2020 Jun; 10(1):10536. PubMed ID: 32601367
[TBL] [Abstract][Full Text] [Related]
19. Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.
Fan M; Liu Z; Xie S; Xu M; Wang S; Gao X; Li L
Phys Med Biol; 2019 Oct; 64(21):215001. PubMed ID: 31470420
[TBL] [Abstract][Full Text] [Related]
20. Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging.
Gao W; Chen J; Zhang B; Wei X; Zhong J; Li X; He X; Zhao F; Chen X
Quant Imaging Med Surg; 2023 Apr; 13(4):2620-2633. PubMed ID: 37064362
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
