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F Laine

Showing results (141-150 of 160) with videos related to

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Proceedings. IEEE International Symposium on Biomedical Imaging|October 14, 2024
UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDYSneha N Naik, Elsa D Angelini, R Graham Barr, et al.
Proceedings. IEEE International Symposium on Biomedical Imaging|October 16, 2024
MULTI-VIEW CNN FOR TOTAL LUNG VOLUME INFERENCE ON CARDIAC COMPUTED TOMOGRAPHYArtur Wysoczanski, Elsa D Angelini, Yifei Sun, et al.
Nature Methods|January 18, 2024
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimagingMartin Priessner, David C A Gaboriau, Arlo Sheridan, et al.
ACS Chemical Biology|June 28, 2019
Fast Fluorescence Lifetime Imaging Reveals the Aggregation Processes of α-Synuclein and Polyglutamine in Aging <i>Caenorhabditis elegans</i>Romain F Laine, Tessa Sinnige, Kai Yu Ma, et al.
Nature Methods|June 2, 2022
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelinesDmitry Ershov, Minh-Son Phan, Joanna W Pylvänäinen, et al.
Annals of the American Thoracic Society|February 15, 2023
Obstructive Sleep Apnea and Longitudinal Changes in Interstitial Lung Imaging and Lung Function: The MESA StudyJohn S Kim, Ali Azarbarzin, Anna J Podolanczuk, et al.
Journal of Physics D: Applied Physics|November 16, 2020
NanoJ: a high-performance open-source super-resolution microscopy toolboxRomain F Laine, Kalina L Tosheva, Nils Gustafsson, et al.
Elife|May 4, 2019
Intrinsically aggregation-prone proteins form amyloid-like aggregates and contribute to tissue aging in <i>Caenorhabditis elegans</i>Chaolie Huang, Sara Wagner-Valladolid, Amberley D Stephens, et al.
Frontiers in Aging Neuroscience|August 29, 2022
Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brainsChen Liu, Nanyan Zhu, Haoran Sun, et al.
Medrxiv : the Preprint Server for Health Sciences|December 8, 2025
Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial IntelligenceSrilaxmi Bearelly, Cassandra Areff, Matthew K Hoffman, et al.
Pageof 16

Showing results (141-150 of 160) with videos related to

Sort By:
Pageof 16
Proceedings. IEEE International Symposium on Biomedical Imaging|October 14, 2024
UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDYSneha N Naik, Elsa D Angelini, R Graham Barr, et al.
Proceedings. IEEE International Symposium on Biomedical Imaging|October 16, 2024
MULTI-VIEW CNN FOR TOTAL LUNG VOLUME INFERENCE ON CARDIAC COMPUTED TOMOGRAPHYArtur Wysoczanski, Elsa D Angelini, Yifei Sun, et al.
Nature Methods|January 18, 2024
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimagingMartin Priessner, David C A Gaboriau, Arlo Sheridan, et al.
ACS Chemical Biology|June 28, 2019
Fast Fluorescence Lifetime Imaging Reveals the Aggregation Processes of α-Synuclein and Polyglutamine in Aging <i>Caenorhabditis elegans</i>Romain F Laine, Tessa Sinnige, Kai Yu Ma, et al.
Nature Methods|June 2, 2022
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelinesDmitry Ershov, Minh-Son Phan, Joanna W Pylvänäinen, et al.
Annals of the American Thoracic Society|February 15, 2023
Obstructive Sleep Apnea and Longitudinal Changes in Interstitial Lung Imaging and Lung Function: The MESA StudyJohn S Kim, Ali Azarbarzin, Anna J Podolanczuk, et al.
Journal of Physics D: Applied Physics|November 16, 2020
NanoJ: a high-performance open-source super-resolution microscopy toolboxRomain F Laine, Kalina L Tosheva, Nils Gustafsson, et al.
Elife|May 4, 2019
Intrinsically aggregation-prone proteins form amyloid-like aggregates and contribute to tissue aging in <i>Caenorhabditis elegans</i>Chaolie Huang, Sara Wagner-Valladolid, Amberley D Stephens, et al.
Frontiers in Aging Neuroscience|August 29, 2022
Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brainsChen Liu, Nanyan Zhu, Haoran Sun, et al.
Medrxiv : the Preprint Server for Health Sciences|December 8, 2025
Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial IntelligenceSrilaxmi Bearelly, Cassandra Areff, Matthew K Hoffman, et al.
Pageof 16