Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Alexandre Cadrin-Chênevert"'
Autor:
Yue Qi, Pedro Vianna, Alexandre Cadrin-Chênevert, Katleen Blanchet, Emmanuel Montagnon, Eugene Belilovsky, Guy Wolf, Louis-Antoine Mullie, Guy Cloutier, Michaël Chassé, An Tang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private d
Externí odkaz:
https://doaj.org/article/22e256b2f8f04da4a8ee480139814455
Autor:
Emmanuel Montagnon, Milena Cerny, Alexandre Cadrin-Chênevert, Vincent Hamilton, Thomas Derennes, André Ilinca, Franck Vandenbroucke-Menu, Simon Turcotte, Samuel Kadoury, An Tang
Publikováno v:
Insights into Imaging, Vol 11, Iss 1, Pp 1-15 (2020)
Abstract Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This
Externí odkaz:
https://doaj.org/article/53a3357b40dd4212aebbd9c8ecb4999e
Autor:
Emmanuel Montagnon, Milena Cerny, Vincent Hamilton, Thomas Derennes, André Ilinca, Mohamed Elforaici, Gilbert Jabbour, Rafi Edmond, Anni Wu, Francisco Romero, Alexandre Cadrin-Chênevert, Samuel Kadoury, Simon Turcotte, An Tang
Predicting recurrence and survival of patients with upfront resectable colorectal cancer liver metastases (CRLM) is crucial to personalize treatment. The purpose of this work was to determine whether radiomics analysis of baseline computed tomography
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8ff9a852f707ca4d604a58e7deb37b8
https://doi.org/10.21203/rs.3.rs-2762043/v1
https://doi.org/10.21203/rs.3.rs-2762043/v1
Autor:
Alexandre Cadrin-Chênevert
Publikováno v:
Radiol Artif Intell
Autor:
William Tanguay, Philippe Acar, Benjamin Fine, Mohamed Abdolell, Bo Gong, Alexandre Cadrin-Chênevert, Carl Chartrand-Lefebvre, Jean Chalaoui, Andrei Gorgos, Anne Shu-Lei Chin, Julie Prénovault, François Guilbert, Laurent Létourneau-Guillon, Jaron Chong, An Tang
Publikováno v:
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.
Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in
Autor:
Francisco Perdigon Romero, Emmanuel Montagnon, Rikiya Yamashita, Gabriel Chartrand, Ian Pan, Samuel Kadoury, Phillip M. Cheng, An Tang, Alexandre Cadrin-Chênevert
Publikováno v:
RadioGraphics. 41:1427-1445
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image fea
Autor:
Hari Trivedi, Alexandre Cadrin-Chênevert, Nabile M. Safdar, Saptarshi Purkayastha, Ashish Sharma, Priyanshu Sinha, Judy Wawira Gichoya, Imon Banerjee, Pradeeban Kathiravelu, Puneet Sharma
Publikováno v:
Journal of Digital Imaging
Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed
Autor:
Alexandre Cadrin-Chênevert
Publikováno v:
Radiol Artif Intell
Publikováno v:
American Journal of Roentgenology. 213:568-574
OBJECTIVE. We provide overviews of deep learning approaches used by two top-placing teams for the 2018 Radiological Society of North America (RSNA) Pneumonia Detection Challenge. CONCLUSION. Practical applications of deep learning techniques, as well
Autor:
Alexandre Cadrin-Chênevert
Publikováno v:
Radiol Artif Intell
To automate skeletal muscle segmentation in a pediatric population using convolutional neural networks that identify and segment the L3 level at CT.In this retrospective study, two sets of U-Net-based models were developed to identify the L3 level in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00bb01ab7e35fa827d44e560d861e612
https://europepmc.org/articles/PMC8035574/
https://europepmc.org/articles/PMC8035574/