15 citations · 32 across the 16 of their papers we have counts for
16 papers
MetaFood3D: 3D Food Dataset with Nutrition Values
Yuhao Chen, Jiangpeng He, Gautham Vinod +11
Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets…
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Jiangpeng He, Yuhao Chen, Gautham Vinod +16
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…
NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images
Matthew Keller, Chi-en Amy Tai, Yuhao Chen +2
Many aging individuals encounter challenges in effectively tracking their dietary intake, exacerbating their susceptibility to nutrition-related health complications. Self-reportin…
How Much You Ate? Food Portion Estimation on Spoons
Aaryam Sharma, Chris Czarnecki, Yuhao Chen +3
Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring throug…
In The Wild Ellipse Parameter Estimation for Circular Dining Plates and Bowls
Akil Pathiranage, Chris Czarnecki, Yuhao Chen +3
Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view an…
NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches
Chi-en Amy Tai, Matthew Keller, Saeejith Nair +8
Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating, as malnutrition has been directly linked to decreased quality of life.…
Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions
Grant Sinha, Krish Parmar, Hilda Azimi +4
Food image segmentation is an important task that has ubiquitous applications, such as estimating the nutritional value of a plate of food. Although machine learning models have be…
NutritionVerse-Thin: An Optimized Strategy for Enabling Improved Rendering of 3D Thin Food Models
Chi-en Amy Tai, Jason Li, Sriram Kumar +4
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 f…
NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation
Chi-en Amy Tai, Matthew Keller, Mattie Kerrigan +4
77% of adults over 50 want to age in place today, presenting a major challenge to ensuring adequate nutritional intake. It has been reported that one in four older adults that are…
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
Jessy Song, Ashkan Ebadi, Adrian Florea +3
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 prev…
A Trustworthy Framework for Medical Image Analysis with Deep Learning
Kai Ma, Siyuan He, Pengcheng Xi +3
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 cha…
Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography
Kai Ma, Pengcheng Xi, Karim Habashy +3
Building AI models with trustworthiness is important especially in regulated areas such as healthcare. In tackling COVID-19, previous work uses convolutional neural networks as the…
COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos
Hilda Azimi, Ashkan Ebadi, Jessy Song +2
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 p…
Improving Classification Model Performance on Chest X-Rays through Lung Segmentation
Hilda Azimi, Jianxing Zhang, Pengcheng Xi +4
Chest radiography is an effective screening tool for diagnosing pulmonary diseases. In computer-aided diagnosis, extracting the relevant region of interest, i.e., isolating the lun…
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
Siyuan He, Pengcheng Xi, Ashkan Ebadi +2
Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between pe…
COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging
Alexander MacLean, Saad Abbasi, Ashkan Ebadi +6
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…