Application of Physics-Informed Neural Networks for Forward and Inverse Analysis of Pile-Soil Interaction

Autor: Vahab, M., Shahbodagh, B., Haghighat, E., Khalili, N.

The application of the Physics-Informed Neural Networks (PINNs) to forward and inverse analysis of pile-soil interaction problems is presented. The main challenge encountered in the Artificial Neural Network (ANN) modelling of pile-soil interaction i

Externí odkaz: http://arxiv.org/abs/2212.08306

Philometroides khalili n. sp., a new philometrid nematode (Philometridae) from the operculum of the cyprinid fish Labeo rosae in Zimbabwe

Autor: F. Moravec, Sareh Tavakol, Ali Halajian, Wilmien J. Luus-Powell, I. Nyagura

Publikováno v: Helminthologia, Vol 52, Iss 2, Pp 113-117 (2015)

Summary A new nematode species, Philometroides khalili n. sp. (Philometridae), is described from female specimens recovered from the operculum of the freshwater cyprinid fish Labeo rosae Steindachner (Cyprinidae, Cypriniformes) caught in the Bubi Riv

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fecb94d4ec6a907467317ce562761382
https://doaj.org/article/f7a11438d6424dec9af01d7af555120c

Automatic brain tissue segmentation in fetal MRI using convolutional neural networks

Autor: Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., Viergever, M. A., Benders, M. J. N. L., Isgum, I.

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segment

Externí odkaz: http://arxiv.org/abs/1906.04713

Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs.

Autor: Familiar AM; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Khalili N; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Khalili N; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Schuman C; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA., Grove E; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA., Viswanathan K; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Seidlitz J; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.; Lifespan Brain Institute at the Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA., Alexander-Bloch A; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.; Lifespan Brain Institute at the Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA., Zapaishchykova A; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Kann BH; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Vossough A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Division of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Storm PB; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Resnick AC; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Kazerooni AF; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.; AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA, USA., Nabavizadeh A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Publikováno v: AJNR. American journal of neuroradiology [AJNR Am J Neuroradiol] 2024 Nov 12. Date of Electronic Publication: 2024 Nov 12.

Automatic segmentation of the intracranialvolume in fetal MR images

Autor: Khalili, N., Moeskops, P., Claessens, N. H. P., Scherpenzeel, S., Turk, E., de Heus, R., Benders, M. J. N. L., Viergever, M. A., Pluim, J. P. W., Išgum, I.

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is

Externí odkaz: http://arxiv.org/abs/1708.02282