352 related articles for article (PubMed ID: 31375430)
1. Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.
Gao X; Wang X
Diagn Interv Imaging; 2020 Feb; 101(2):91-100. PubMed ID: 31375430
[TBL] [Abstract][Full Text] [Related]
2. Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study.
Gao X; Wang X
Int J Comput Assist Radiol Surg; 2019 Nov; 14(11):1981-1991. PubMed ID: 31555998
[TBL] [Abstract][Full Text] [Related]
3. Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.
Hallinan JTPD; Zhu L; Yang K; Makmur A; Algazwi DAR; Thian YL; Lau S; Choo YS; Eide SE; Yap QV; Chan YH; Tan JH; Kumar N; Ooi BC; Yoshioka H; Quek ST
Radiology; 2021 Jul; 300(1):130-138. PubMed ID: 33973835
[TBL] [Abstract][Full Text] [Related]
4. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Bien N; Rajpurkar P; Ball RL; Irvin J; Park A; Jones E; Bereket M; Patel BN; Yeom KW; Shpanskaya K; Halabi S; Zucker E; Fanton G; Amanatullah DF; Beaulieu CF; Riley GM; Stewart RJ; Blankenberg FG; Larson DB; Jones RH; Langlotz CP; Ng AY; Lungren MP
PLoS Med; 2018 Nov; 15(11):e1002699. PubMed ID: 30481176
[TBL] [Abstract][Full Text] [Related]
5. A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
Maki S; Furuya T; Horikoshi T; Yokota H; Mori Y; Ota J; Kawasaki Y; Miyamoto T; Norimoto M; Okimatsu S; Shiga Y; Inage K; Orita S; Takahashi H; Suyari H; Uno T; Ohtori S
Spine (Phila Pa 1976); 2020 May; 45(10):694-700. PubMed ID: 31809468
[TBL] [Abstract][Full Text] [Related]
6. Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.
Kiryu S; Yasaka K; Akai H; Nakata Y; Sugomori Y; Hara S; Seo M; Abe O; Ohtomo K
Eur Radiol; 2019 Dec; 29(12):6891-6899. PubMed ID: 31264017
[TBL] [Abstract][Full Text] [Related]
7. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.
Liu KL; Wu T; Chen PT; Tsai YM; Roth H; Wu MS; Liao WC; Wang W
Lancet Digit Health; 2020 Jun; 2(6):e303-e313. PubMed ID: 33328124
[TBL] [Abstract][Full Text] [Related]
8. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.
Fu J; Yang Y; Singhrao K; Ruan D; Chu FI; Low DA; Lewis JH
Med Phys; 2019 Sep; 46(9):3788-3798. PubMed ID: 31220353
[TBL] [Abstract][Full Text] [Related]
9. Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.
Gu Z; Dai W; Chen J; Jiang Q; Lin W; Wang Q; Chen J; Gu C; Li J; Ying G; Zhu Y
BMC Cancer; 2024 Mar; 24(1):350. PubMed ID: 38504164
[TBL] [Abstract][Full Text] [Related]
10. Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.
Krishnan AP; Song Z; Clayton D; Gaetano L; Jia X; de Crespigny A; Bengtsson T; Carano RAD
Radiology; 2022 Mar; 302(3):662-673. PubMed ID: 34904871
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
Xia W; Hu B; Li H; Shi W; Tang Y; Yu Y; Geng C; Wu Q; Yang L; Yu Z; Geng D; Li Y
J Magn Reson Imaging; 2021 Sep; 54(3):880-887. PubMed ID: 33694250
[TBL] [Abstract][Full Text] [Related]
13. Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.
Cunha GM; Hasenstab KA; Higaki A; Wang K; Delgado T; Brunsing RL; Schlein A; Schwartzman A; Hsiao A; Sirlin CB; Fowler KJ
Eur J Radiol; 2020 Mar; 124():108837. PubMed ID: 31958630
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.
Mahmutoglu MA; Preetha CJ; Meredig H; Tonn JC; Weller M; Wick W; Bendszus M; Brugnara G; Vollmuth P
Radiol Artif Intell; 2024 Jan; 6(1):e230095. PubMed ID: 38166331
[TBL] [Abstract][Full Text] [Related]
15. Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer.
Fan M; Cao X; Lü F; Xie S; Yu Z; Chen Y; Lü Z; Li L
Phys Med Biol; 2024 Apr; 69(9):. PubMed ID: 38537294
[No Abstract] [Full Text] [Related]
16. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.
Banzato T; Causin F; Della Puppa A; Cester G; Mazzai L; Zotti A
J Magn Reson Imaging; 2019 Oct; 50(4):1152-1159. PubMed ID: 30896065
[TBL] [Abstract][Full Text] [Related]
17. Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.
Sujit SJ; Coronado I; Kamali A; Narayana PA; Gabr RE
J Magn Reson Imaging; 2019 Oct; 50(4):1260-1267. PubMed ID: 30811739
[TBL] [Abstract][Full Text] [Related]
18. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.
Yin HL; Jiang Y; Xu Z; Jia HH; Lin GW
J Cancer Res Clin Oncol; 2023 Jun; 149(6):2575-2584. PubMed ID: 35771263
[TBL] [Abstract][Full Text] [Related]
19. Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.
Gao P; Shan W; Guo Y; Wang Y; Sun R; Cai J; Li H; Chan WS; Liu P; Yi L; Zhang S; Li W; Jiang T; He K; Wu Z
JAMA Netw Open; 2022 Aug; 5(8):e2225608. PubMed ID: 35939301
[TBL] [Abstract][Full Text] [Related]
20. Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.
Aldoj N; Lukas S; Dewey M; Penzkofer T
Eur Radiol; 2020 Feb; 30(2):1243-1253. PubMed ID: 31468158
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
