نتائج البحث - "David Romo-Bucheli"

المصدر: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021

مصطلحات موضوعية: Heart Diseases, Image Processing, Computer-Assisted, Humans, Magnetic Resonance Imaging, Cine, Heart, Magnetic Resonance Imaging

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a6a2a05947a71edd05b860c054fb989
https://pubmed.ncbi.nlm.nih.gov/34892386

4

A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans

المؤلفون: Lina Vasquez, Diana Valenzuela, Carolina Valenzuela, Jefferson Rodriguez, Franklin Sierra, Fabio Martinez, David Romo-Bucheli, Paul Camacho, Daniel Mantilla

المصدر: ISBI

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::d5f7edb3ec1c7f34ebd3cb89afed3eee
https://doi.org/10.1109/isbi48211.2021.9434154

5

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers

المؤلفون: David L. Rimm, Cheng Lu, Hannah Gilmore, David Romo-Bucheli, Shridar Ganesan, Anant Madabhushi, Xiangxue Wang, Andrew Janowczyk

المصدر: Laboratory Investigation. 98:1438-1448

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::024a23a22ec21d71fda12447cfb2bf1d
https://doi.org/10.1038/s41374-018-0095-7

Find this article in full text from Springer Verlag.

Find this article in full text from ProQuest

6

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging

المؤلفون: Hrvoje Bogunovic, David Romo-Bucheli, Ursula Schmidt Erfurth

المصدر: IEEE journal of biomedical and health informatics. 24(12)

مصطلحات موضوعية: 0301 basic medicine, Computer science, Retina, 03 medical and health sciences, Macular Degeneration, 0302 clinical medicine, Deep Learning, Health Information Management, Optical coherence tomography, Image Interpretation, Computer-Assisted, medicine, Medical imaging, Humans, Electrical and Electronic Engineering, medicine.diagnostic_test, Artificial neural network, business.industry, Deep learning, Pattern recognition, Computer Science Applications, Visualization, 030104 developmental biology, Recurrent neural network, Test set, 030221 ophthalmology & optometry, Disease Progression, Artificial intelligence, business, Algorithms, Tomography, Optical Coherence, Biotechnology, Companion diagnostic

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbc3b1e1e7678221c6cb207ef954d234
https://pubmed.ncbi.nlm.nih.gov/32750929

7

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina

المؤلفون: Sebastian M. Waldstein, Ursula Schmidt-Erfurth, Hrvoje Bogunovic, José Ignacio Orlando, David Romo-Bucheli, Philipp Seeböck, Bianca S. Gerendas

المصدر: Biomedical optics express. 11(1)

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::150405db701ad5494791b4c33f728321
https://pubmed.ncbi.nlm.nih.gov/32010521

8

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers

المؤلفون: Andrew Janowczyk, Eduardo Romero, Anant Madabhushi, Hannah Gilmore, David Romo-Bucheli

المصدر: Cytometry Part A. 91:566-573

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::aa8ba66a14ce5f2c57102597f85601ef
https://doi.org/10.1002/cyto.a.23065

View record at Wiley

9

Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation

المؤلفون: Ursula Schmidt-Erfurth, Hrvoje Bogunovic, Georg Langs, José Ignacio Orlando, Philipp Seeböck, Bianca S. Gerendas, Sebastian M. Waldstein, David Romo-Bucheli

المصدر: ISBI

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ee21c3821b39fac17502bba0c015c57
https://doi.org/10.1109/isbi.2019.8759158

10

Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model

المؤلفون: Angélica Atehortúa, Mireille Garreau, David Romo-Bucheli, Eduardo Romero

المساهمون: Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universidad Nacional de Colombia [Bogotà] (UNAL), Région Bretagne 647 (2015Labex ANR-11-LABX-0004, Li S.McLeod K.Young A.Rhode K.Pop M.Zhao J.Mansi T.Sermesant M. (eds), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-11-LABX-0004,CAMI,Gestes Médico-Chirurgicaux Assistés par Ordinateur(2011)

المصدر: 9th International Workshop on Statistical Atlases and Computational Models of the Heart Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
9th International Workshop on Statistical Atlases and Computational Models of the Heart Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Sep 2018, Granada, Spain. pp.439-449, ⟨10.1007/978-3-030-12029-0_47⟩
Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges ISBN: 9783030120283
STACOM@MICCAI

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14f7f0344f043115119e8bb81d5b5918
https://hal-univ-rennes1.archives-ouvertes.fr/hal-02121139

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