BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

156 related articles for article (PubMed ID: 36553140)

  • 1. Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography.
    Li X; Cui J; Song J; Jia M; Zou Z; Ding G; Zheng Y
    Diagnostics (Basel); 2022 Dec; 12(12):. PubMed ID: 36553140
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification of contrast-enhanced spectral mammography (CESM) images.
    Perek S; Kiryati N; Zimmerman-Moreno G; Sklair-Levy M; Konen E; Mayer A
    Int J Comput Assist Radiol Surg; 2019 Feb; 14(2):249-257. PubMed ID: 30367322
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multiview multimodal network for breast cancer diagnosis in contrast-enhanced spectral mammography images.
    Song J; Zheng Y; Zakir Ullah M; Wang J; Jiang Y; Xu C; Zou Z; Ding G
    Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):979-988. PubMed ID: 33966155
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving the classification ability of network utilizing fusion technique in contrast-enhanced spectral mammography.
    Song J; Zheng Y; Xu C; Zou Z; Ding G; Huang W
    Med Phys; 2022 Feb; 49(2):966-977. PubMed ID: 34860417
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
    Patel BK; Ranjbar S; Wu T; Pockaj BA; Li J; Zhang N; Lobbes M; Zhang B; Mitchell JR
    Eur J Radiol; 2018 Jan; 98():207-213. PubMed ID: 29279165
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images.
    Fanizzi A; Losurdo L; Basile TMA; Bellotti R; Bottigli U; Delogu P; Diacono D; Didonna V; Fausto A; Lombardi A; Lorusso V; Massafra R; Tangaro S; La Forgia D
    J Clin Med; 2019 Jun; 8(6):. PubMed ID: 31234363
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion.
    Zhuang Z; Yang Z; Raj ANJ; Wei C; Jin P; Zhuang S
    Comput Methods Programs Biomed; 2021 Sep; 208():106221. PubMed ID: 34144251
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Optimized Radial Basis Neural Network for Classification of Breast Cancer Images.
    Rajathi GM
    Curr Med Imaging; 2021; 17(1):97-108. PubMed ID: 32416697
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
    Lee H; Hong H; Kim J; Jung DC
    Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
    Qiu Y; Yan S; Gundreddy RR; Wang Y; Cheng S; Liu H; Zheng B
    J Xray Sci Technol; 2017; 25(5):751-763. PubMed ID: 28436410
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [Clinical value of suspicious calcification in the diagnosis and surgical treatment of breast lesions using contrast-enhanced spectral mammography].
    Sun XF; Xing W; Yu SN; Sha YY; Pan L; Chen Q
    Zhonghua Yi Xue Za Zhi; 2020 Jan; 100(1):42-46. PubMed ID: 31914557
    [No Abstract]   [Full Text] [Related]  

  • 12. Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.
    Dominique C; Callonnec F; Berghian A; Defta D; Vera P; Modzelewski R; Decazes P
    Eur Radiol; 2022 Jul; 32(7):4834-4844. PubMed ID: 35094119
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of the Mammography, Contrast-Enhanced Spectral Mammography and Ultrasonography in a Group of 116 patients.
    LuczyƄska E; Heinze S; Adamczyk A; Rys J; Mitus JW; Hendrick E
    Anticancer Res; 2016 Aug; 36(8):4359-66. PubMed ID: 27466557
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-scale attention-based convolutional neural network for classification of breast masses in mammograms.
    Niu J; Li H; Zhang C; Li D
    Med Phys; 2021 Jul; 48(7):3878-3892. PubMed ID: 33982807
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images.
    Massafra R; Bove S; Lorusso V; Biafora A; Comes MC; Didonna V; Diotaiuti S; Fanizzi A; Nardone A; Nolasco A; Ressa CM; Tamborra P; Terenzio A; La Forgia D
    Diagnostics (Basel); 2021 Apr; 11(4):. PubMed ID: 33920221
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computer-aided diagnosis of prostate cancer based on deep neural networks from multi-parametric magnetic resonance imaging.
    Yi Z; Ou Z; Hu J; Qiu D; Quan C; Othmane B; Wang Y; Wu L
    Front Physiol; 2022; 13():918381. PubMed ID: 36105290
    [No Abstract]   [Full Text] [Related]  

  • 17. Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.
    Ukwuoma CC; Hossain MA; Jackson JK; Nneji GU; Monday HN; Qin Z
    Diagnostics (Basel); 2022 May; 12(5):. PubMed ID: 35626307
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.
    Feng H; Cao J; Wang H; Xie Y; Yang D; Feng J; Chen B
    Magn Reson Imaging; 2020 Jun; 69():40-48. PubMed ID: 32173583
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.
    Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Choi MT; Han SM; Kim TS
    Comput Methods Programs Biomed; 2018 Apr; 157():85-94. PubMed ID: 29477437
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.
    Sakai A; Onishi Y; Matsui M; Adachi H; Teramoto A; Saito K; Fujita H
    Radiol Phys Technol; 2020 Mar; 13(1):27-36. PubMed ID: 31686300
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.