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.
138 related articles for article (PubMed ID: 38036638)
1. Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection. Raab F; Malloni W; Wein S; Greenlee MW; Lang EW Sci Rep; 2023 Nov; 13(1):21154. PubMed ID: 38036638 [TBL] [Abstract][Full Text] [Related]
2. Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Aslani S; Dayan M; Storelli L; Filippi M; Murino V; Rocca MA; Sona D Neuroimage; 2019 Aug; 196():1-15. PubMed ID: 30953833 [TBL] [Abstract][Full Text] [Related]
3. A dense residual U-net for multiple sclerosis lesions segmentation from multi-sequence 3D MR images. Sarica B; Seker DZ; Bayram B Int J Med Inform; 2023 Feb; 170():104965. PubMed ID: 36580821 [TBL] [Abstract][Full Text] [Related]
4. 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]
6. Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability. Opfer R; Krüger J; Spies L; Ostwaldt AC; Kitzler HH; Schippling S; Buchert R Eur Radiol; 2023 Mar; 33(3):1852-1861. PubMed ID: 36264314 [TBL] [Abstract][Full Text] [Related]
7. Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks. Krüger J; Ostwaldt AC; Spies L; Geisler B; Schlaefer A; Kitzler HH; Schippling S; Opfer R Eur Radiol; 2022 Apr; 32(4):2798-2809. PubMed ID: 34643779 [TBL] [Abstract][Full Text] [Related]
8. Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. McKinley R; Wepfer R; Aschwanden F; Grunder L; Muri R; Rummel C; Verma R; Weisstanner C; Reyes M; Salmen A; Chan A; Wagner F; Wiest R Sci Rep; 2021 Jan; 11(1):1087. PubMed ID: 33441684 [TBL] [Abstract][Full Text] [Related]
9. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Valverde S; Cabezas M; Roura E; González-Villà S; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Oliver A; Lladó X Neuroimage; 2017 Jul; 155():159-168. PubMed ID: 28435096 [TBL] [Abstract][Full Text] [Related]
10. ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation. Zhang H; Zhang J; Li C; Sweeney EM; Spincemaille P; Nguyen TD; Gauthier SA; Wang Y; Marcille M Neuroimage Clin; 2021; 32():102854. PubMed ID: 34666289 [TBL] [Abstract][Full Text] [Related]
11. One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. Valverde S; Salem M; Cabezas M; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Salvi J; Oliver A; Lladó X Neuroimage Clin; 2019; 21():101638. PubMed ID: 30555005 [TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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 Neuroimage Clin; 2024; 42():103611. PubMed ID: 38703470 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. Brosch T; Tang LY; Youngjin Yoo ; Li DK; Traboulsee A; Tam R IEEE Trans Med Imaging; 2016 May; 35(5):1229-1239. PubMed ID: 26886978 [TBL] [Abstract][Full Text] [Related]
16. 3D whole brain segmentation using spatially localized atlas network tiles. Huo Y; Xu Z; Xiong Y; Aboud K; Parvathaneni P; Bao S; Bermudez C; Resnick SM; Cutting LE; Landman BA Neuroimage; 2019 Jul; 194():105-119. PubMed ID: 30910724 [TBL] [Abstract][Full Text] [Related]
17. Convolutional neural network for automated mass segmentation in mammography. Abdelhafiz D; Bi J; Ammar R; Yang C; Nabavi S BMC Bioinformatics; 2020 Dec; 21(Suppl 1):192. PubMed ID: 33297952 [TBL] [Abstract][Full Text] [Related]
18. Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning. Narayana PA; Coronado I; Sujit SJ; Sun X; Wolinsky JS; Gabr RE Magn Reson Imaging; 2020 Jan; 65():8-14. PubMed ID: 31670238 [TBL] [Abstract][Full Text] [Related]
19. Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network. Ansari SU; Javed K; Qaisar SM; Jillani R; Haider U J Healthc Eng; 2021; 2021():4138137. PubMed ID: 34484652 [TBL] [Abstract][Full Text] [Related]