Search Results for author:
Found 13 papers, 3 papers with code
Tune without Validation: Searching for Learning Rate and Weight Decay on Training Sets
We introduce Tune without Validation (Twin), a pipeline for tuning learning rate and weight decay without validation sets.
No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets
Solving image classification tasks given small training datasets remains an open challenge for modern computer vision.
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations.
Food Recognition and Nutritional Apps
Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet.
Partially Supervised Multi-Task Network for Single-View Dietary Assessment
Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved.
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition.
Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries
Direct computer vision based-nutrient content estimation is a demanding task, due to deformation and occlusions of ingredients, as well as high intra-class and low inter-class variability between meal classes.
U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets
We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks.
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Image registration and in particular deformable registration methods are pillars of medical imaging.
A Multi-Task Learning Approach for Meal Assessment
Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet.
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs.
Two-view 3D Reconstruction for Food Volume Estimation
The increasing prevalence of diet-related chronic diseases coupled with the ineffectiveness of traditional diet management methods have resulted in a need for novel tools to accurately and automatically assess meals.
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem.
