These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

331 related articles for article (PubMed ID: 33045323)

  • 1. 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]  

  • 2. Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks.
    Fujioka T; Katsuta L; Kubota K; Mori M; Kikuchi Y; Kato A; Oda G; Nakagawa T; Kitazume Y; Tateishi U
    Ultrason Imaging; 2020; 42(4-5):213-220. PubMed ID: 32501152
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images.
    Adachi M; Fujioka T; Mori M; Kubota K; Kikuchi Y; Xiaotong W; Oyama J; Kimura K; Oda G; Nakagawa T; Uetake H; Tateishi U
    Diagnostics (Basel); 2020 May; 10(5):. PubMed ID: 32443922
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
    Fujioka T; Kubota K; Mori M; Kikuchi Y; Katsuta L; Kasahara M; Oda G; Ishiba T; Nakagawa T; Tateishi U
    Jpn J Radiol; 2019 Jun; 37(6):466-472. PubMed ID: 30888570
    [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 method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.
    Ozaki J; Fujioka T; Yamaga E; Hayashi A; Kujiraoka Y; Imokawa T; Takahashi K; Okawa S; Yashima Y; Mori M; Kubota K; Oda G; Nakagawa T; Tateishi U
    Jpn J Radiol; 2022 Aug; 40(8):814-822. PubMed ID: 35284996
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.
    Fukuda T; Tsunoda H; Yagishita K; Naganawa S; Hayashi K; Kurihara Y
    Ultrasound Med Biol; 2023 Apr; 49(4):989-995. PubMed ID: 36681608
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.
    Truhn D; Schrading S; Haarburger C; Schneider H; Merhof D; Kuhl C
    Radiology; 2019 Feb; 290(2):290-297. PubMed ID: 30422086
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images.
    Zhou J; Luo LY; Dou Q; Chen H; Chen C; Li GJ; Jiang ZF; Heng PA
    J Magn Reson Imaging; 2019 Oct; 50(4):1144-1151. PubMed ID: 30924997
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI.
    Wong LM; King AD; Ai QYH; Lam WKJ; Poon DMC; Ma BBY; Chan KCA; Mo FKF
    Eur Radiol; 2021 Jun; 31(6):3856-3863. PubMed ID: 33241522
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
    Wang Y; Choi EJ; Choi Y; Zhang H; Jin GY; Ko SB
    Ultrasound Med Biol; 2020 May; 46(5):1119-1132. PubMed ID: 32059918
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.
    Mao N; Zhang H; Dai Y; Li Q; Lin F; Gao J; Zheng T; Zhao F; Xie H; Xu C; Ma H
    Br J Cancer; 2023 Mar; 128(5):793-804. PubMed ID: 36522478
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
    Haenssle HA; Fink C; Schneiderbauer R; Toberer F; Buhl T; Blum A; Kalloo A; Hassen ABH; Thomas L; Enk A; Uhlmann L; ; Alt C; Arenbergerova M; Bakos R; Baltzer A; Bertlich I; Blum A; Bokor-Billmann T; Bowling J; Braghiroli N; Braun R; Buder-Bakhaya K; Buhl T; Cabo H; Cabrijan L; Cevic N; Classen A; Deltgen D; Fink C; Georgieva I; Hakim-Meibodi LE; Hanner S; Hartmann F; Hartmann J; Haus G; Hoxha E; Karls R; Koga H; Kreusch J; Lallas A; Majenka P; Marghoob A; Massone C; Mekokishvili L; Mestel D; Meyer V; Neuberger A; Nielsen K; Oliviero M; Pampena R; Paoli J; Pawlik E; Rao B; Rendon A; Russo T; Sadek A; Samhaber K; Schneiderbauer R; Schweizer A; Toberer F; Trennheuser L; Vlahova L; Wald A; Winkler J; Wölbing P; Zalaudek I
    Ann Oncol; 2018 Aug; 29(8):1836-1842. PubMed ID: 29846502
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.
    Zhao HB; Liu C; Ye J; Chang LF; Xu Q; Shi BW; Liu LL; Yin YL; Shi BB
    Endokrynol Pol; 2021; 72(3):217-225. PubMed ID: 33619712
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.
    Heo SJ; Kim Y; Yun S; Lim SS; Kim J; Nam CM; Park EC; Jung I; Yoon JH
    Int J Environ Res Public Health; 2019 Jan; 16(2):. PubMed ID: 30654560
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.
    Hamm CA; Wang CJ; Savic LJ; Ferrante M; Schobert I; Schlachter T; Lin M; Duncan JS; Weinreb JC; Chapiro J; Letzen B
    Eur Radiol; 2019 Jul; 29(7):3338-3347. PubMed ID: 31016442
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.
    Zhang M; He G; Pan C; Yun B; Shen D; Meng M
    J Cancer Res Ther; 2023 Dec; 19(6):1589-1596. PubMed ID: 38156926
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 17.