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 *

137 related articles for article (PubMed ID: 36340756)

  • 1. New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation.
    Basaran BD; Matthews PM; Bai W
    Front Neurosci; 2022; 16():1007453. PubMed ID: 36340756
    [TBL] [Abstract][Full Text] [Related]  

  • 2. New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images.
    Sarica B; Seker DZ
    Front Neurosci; 2022; 16():912000. PubMed ID: 35968389
    [TBL] [Abstract][Full Text] [Related]  

  • 3. CarveMix: A simple data augmentation method for brain lesion segmentation.
    Zhang X; Liu C; Ou N; Zeng X; Zhuo Z; Duan Y; Xiong X; Yu Y; Liu Z; Liu Y; Ye C
    Neuroimage; 2023 May; 271():120041. PubMed ID: 36933626
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies.
    Hitziger S; Ling WX; Fritz T; D'Albis T; Lemke A; Grilo J
    Front Neurosci; 2022; 16():964250. PubMed ID: 36033604
    [TBL] [Abstract][Full Text] [Related]  

  • 5. New multiple sclerosis lesion segmentation and detection using pre-activation U-Net.
    Ashtari P; Barile B; Van Huffel S; Sappey-Marinier D
    Front Neurosci; 2022; 16():975862. PubMed ID: 36389254
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection.
    Hashemi SR; Salehi SSM; Erdogmus D; Prabhu SP; Warfield SK; Gholipour A
    IEEE Access; 2019; 7():721-1735. PubMed ID: 31528523
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach.
    Salem M; Ryan MA; Oliver A; Hussain KF; Lladó X
    Front Neurosci; 2022; 16():1007619. PubMed ID: 36507318
    [TBL] [Abstract][Full Text] [Related]  

  • 8. LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.
    Wiltgen T; McGinnis J; Schlaeger S; Kofler F; Voon C; Berthele A; Bischl D; Grundl L; Will N; Metz M; Schinz D; Sepp D; Prucker P; Schmitz-Koep B; Zimmer C; Menze B; Rueckert D; Hemmer B; Kirschke J; Mühlau M; Wiestler B
    medRxiv; 2024 Mar; ():. PubMed ID: 38045345
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.
    Salem M; Valverde S; Cabezas M; Pareto D; Oliver A; Salvi J; Rovira À; Lladó X
    Neuroimage Clin; 2020; 25():102149. PubMed ID: 31918065
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.
    Essa E; Aldesouky D; Hussein SE; Rashad MZ
    Med Biol Eng Comput; 2020 Sep; 58(9):2161-2175. PubMed ID: 32681214
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients.
    de Oliveira M; Piacenti-Silva M; da Rocha FCG; Santos JM; Cardoso JDS; Lisboa-Filho PN
    Diagnostics (Basel); 2022 Jan; 12(2):. PubMed ID: 35204321
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic segmentation and grading of ankylosing spondylitis on MR images via lightweight hybrid multi-scale convolutional neural network with reinforcement learning.
    Gou S; Lu Y; Tong N; Huang L; Liu N; Han Q
    Phys Med Biol; 2021 Oct; 66(20):. PubMed ID: 34517352
    [No Abstract]   [Full Text] [Related]  

  • 13. Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images.
    Freire PG; Ferrari RJ
    Comput Biol Med; 2016 Jun; 73():10-23. PubMed ID: 27058437
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.
    Yoo Y; Gibson E; Zhao G; Re TJ; Parmar H; Das J; Wang H; Kim MM; Shen C; Lee Y; Kondziolka D; Ibrahim M; Lian J; Jain R; Zhu T; Comaniciu D; Balter JM; Cao Y
    Int J Radiat Oncol Biol Phys; 2024 Jul; ():. PubMed ID: 39059508
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Boosting multiple sclerosis lesion segmentation through attention mechanism.
    Rondinella A; Crispino E; Guarnera F; Giudice O; Ortis A; Russo G; Di Lorenzo C; Maimone D; Pappalardo F; Battiato S
    Comput Biol Med; 2023 Jul; 161():107021. PubMed ID: 37216775
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Longitudinal detection of new MS lesions using deep learning.
    Kamraoui RA; Mansencal B; Manjon JV; Coupé P
    Front Neuroimaging; 2022; 1():948235. PubMed ID: 37555158
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks.
    Krüger J; Opfer R; Gessert N; Ostwaldt AC; Manogaran P; Kitzler HH; Schlaefer A; Schippling S
    Neuroimage Clin; 2020; 28():102445. PubMed ID: 33038667
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet.
    Chen Y; Xing L; Yu L; Bagshaw HP; Buyyounouski MK; Han B
    Med Phys; 2020 Dec; 47(12):6421-6429. PubMed ID: 33012016
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging.
    García-Lorenzo D; Francis S; Narayanan S; Arnold DL; Collins DL
    Med Image Anal; 2013 Jan; 17(1):1-18. PubMed ID: 23084503
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Pseudo-Label Assisted nnU-Net enables automatic segmentation of 7T MRI from a single acquisition.
    Donnay C; Dieckhaus H; Tsagkas C; Gaitán MI; Beck ES; Mullins A; Reich DS; Nair G
    Front Neuroimaging; 2023; 2():1252261. PubMed ID: 38107773
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.