no code implementations • 14 Sep 2023 • Chi-en Amy Tai, Matthew Keller, Saeejith Nair, Yuhao Chen, Yifan Wu, Olivia Markham, Krish Parmar, Pengcheng Xi, Heather Keller, Sharon Kirkpatrick, Alexander Wong
Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images, but the lack of comprehensive datasets with diverse viewpoints, modalities and food annotations hinders the accuracy and realism of such methods.
no code implementations • 15 Jun 2023 • Grant Sinha, Krish Parmar, Hilda Azimi, Amy Tai, Yuhao Chen, Alexander Wong, Pengcheng Xi
To address these issues, two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional Encoder representation for Image Transformers (BEiT).
no code implementations • 12 Apr 2023 • Chi-en Amy Tai, Jason Li, Sriram Kumar, Saeejith Nair, Yuhao Chen, Pengcheng Xi, Alexander Wong
With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as food printing, nutrition prediction, or management of food wastage.
no code implementations • 12 Apr 2023 • Chi-en Amy Tai, Matthew Keller, Mattie Kerrigan, Yuhao Chen, Saeejith Nair, Pengcheng Xi, Alexander Wong
Unlike existing datasets, a collection of 3D models with nutritional information allow for view synthesis to create an infinite number of 2D images for any given viewpoint/camera angle along with the associated nutritional information.
no code implementations • 4 Jan 2023 • Jessy Song, Ashkan Ebadi, Adrian Florea, Pengcheng Xi, Stéphane Tremblay, Alexander Wong
As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity.
no code implementations • 6 Dec 2022 • Kai Ma, Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stéphane Tremblay, Alexander Wong
Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy.
no code implementations • 19 Jul 2022 • Kai Ma, Pengcheng Xi, Karim Habashy, Ashkan Ebadi, Stéphane Tremblay, Alexander Wong
In this study, we propose a feature learning approach using Vision Transformers, which use an attention-based mechanism, and examine the representation learning capability of Transformers as a new backbone architecture for medical imaging.
no code implementations • 18 May 2022 • Hilda Azimi, Ashkan Ebadi, Jessy Song, Pengcheng Xi, Alexander Wong
Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety.
no code implementations • 22 Feb 2022 • Hilda Azimi, Jianxing Zhang, Pengcheng Xi, Hala Asad, Ashkan Ebadi, Stephane Tremblay, Alexander Wong
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
no code implementations • 14 Dec 2021 • Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stephane Tremblay, Alexander Wong
Ablation study is conducted for both the performance and the trust on feature learning methods and loss functions.
1 code implementation • 5 Aug 2021 • Alexander MacLean, Saad Abbasi, Ashkan Ebadi, Andy Zhao, Maya Pavlova, Hayden Gunraj, Pengcheng Xi, Sonny Kohli, Alexander Wong
The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus.
no code implementations • 4 May 2021 • Jianxing Zhang, Pengcheng Xi, Ashkan Ebadi, Hilda Azimi, Stephane Tremblay, Alexander Wong
The COVID-19 pandemic has had devastating effects on the well-being of the global population.
2 code implementations • 18 Mar 2021 • Ashkan Ebadi, Pengcheng Xi, Alexander MacLean, Stéphane Tremblay, Sonny Kohli, Alexander Wong
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population.
no code implementations • 22 Jul 2020 • Ashkan Ebadi, Pengcheng Xi, Stéphane Tremblay, Bruce Spencer, Raman Pall, Alexander Wong
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences.
no code implementations • 5 Mar 2018 • Pengcheng Xi, Chang Shu, Rafik Goubran
State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities.