BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

310 related articles for article (PubMed ID: 35009788)

  • 1. Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach.
    Oh S; Kim YJ; Park YT; Kim KG
    Sensors (Basel); 2021 Dec; 22(1):. PubMed ID: 35009788
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach.
    Seo K; Lim JH; Seo J; Nguon LS; Yoon H; Park JS; Park S
    Cancers (Basel); 2022 Oct; 14(20):. PubMed ID: 36291895
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Endoscopic ultrasound diagnosis system based on deep learning in images capture and segmentation training of solid pancreatic masses.
    Tang A; Gong P; Fang N; Ye M; Hu S; Liu J; Wang W; Gao K; Wang X; Tian L
    Med Phys; 2023 Jul; 50(7):4197-4205. PubMed ID: 36965116
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automatic Segmentation of Pancreatic Tumors Using Deep Learning on a Video Image of Contrast-Enhanced Endoscopic Ultrasound.
    Iwasa Y; Iwashita T; Takeuchi Y; Ichikawa H; Mita N; Uemura S; Shimizu M; Kuo YT; Wang HP; Hara T
    J Clin Med; 2021 Aug; 10(16):. PubMed ID: 34441883
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.
    Zhu HT; Zhang XY; Shi YJ; Li XT; Sun YS
    J Appl Clin Med Phys; 2021 Sep; 22(9):324-331. PubMed ID: 34343402
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography.
    Verhelst PJ; Smolders A; Beznik T; Meewis J; Vandemeulebroucke A; Shaheen E; Van Gerven A; Willems H; Politis C; Jacobs R
    J Dent; 2021 Nov; 114():103786. PubMed ID: 34425172
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automated 3D U-net based segmentation of neonatal cerebral ventricles from 3D ultrasound images.
    Szentimrey Z; de Ribaupierre S; Fenster A; Ukwatta E
    Med Phys; 2022 Feb; 49(2):1034-1046. PubMed ID: 34958147
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.
    Lin Z; Cui Y; Liu J; Sun Z; Ma S; Zhang X; Wang X
    Eur Radiol; 2021 Jul; 31(7):5021-5031. PubMed ID: 33439313
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.
    Soleimani P; Farezi N
    Sci Rep; 2023 Nov; 13(1):19808. PubMed ID: 37957203
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm.
    Su F; Zhang W; Liu Y; Chen S; Lin M; Feng M; Yin J; Tan L; Shen Y
    J Gastrointest Oncol; 2023 Oct; 14(5):1982-1992. PubMed ID: 37969831
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography; CT myocardium segmentation.
    Jun Guo B; He X; Lei Y; Harms J; Wang T; Curran WJ; Liu T; Jiang Zhang L; Yang X
    Med Phys; 2020 Apr; 47(4):1775-1785. PubMed ID: 32017118
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images.
    Huang A; Jiang L; Zhang J; Wang Q
    Quant Imaging Med Surg; 2022 Jun; 12(6):3138-3150. PubMed ID: 35655843
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.
    Orlando N; Gillies DJ; Gyacskov I; Romagnoli C; D'Souza D; Fenster A
    Med Phys; 2020 Jun; 47(6):2413-2426. PubMed ID: 32166768
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy.
    Yeung M; Sala E; Schönlieb CB; Rundo L
    Comput Biol Med; 2021 Oct; 137():104815. PubMed ID: 34507156
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation.
    Apivanichkul K; Phasukkit P; Dankulchai P; Sittiwong W; Jitwatcharakomol T
    Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420884
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
    Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
    Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.
    Zeng Y; Tsui PH; Wu W; Zhou Z; Wu S
    J Digit Imaging; 2021 Feb; 34(1):134-148. PubMed ID: 33483862
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.
    Luo X; Li P; Chen H; Zhou K; Piao S; Yang L; Hu B; Geng D
    Phys Med Biol; 2024 Mar; 69(6):. PubMed ID: 38330492
    [No Abstract]   [Full Text] [Related]  

  • 19. Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning.
    Huang L; Kurz C; Freislederer P; Manapov F; Corradini S; Niyazi M; Belka C; Landry G; Riboldi M
    Med Phys; 2024 Mar; 51(3):1957-1973. PubMed ID: 37683107
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RA V-Net: deep learning network for automated liver segmentation.
    Lee Z; Qi S; Fan C; Xie Z; Meng J
    Phys Med Biol; 2022 Jun; 67(12):. PubMed ID: 35588720
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 16.