297 citations · 592 across the 17 of their papers we have counts for
17 papers
A SAM based Tool for Semi-Automatic Food Annotation
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato +1
The advancement of artificial intelligence (AI) in food and nutrition research is hindered by a critical bottleneck: the lack of annotated food data. Despite the rise of highly eff…
Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
Lorenzo Brigato, Stavroula Mougiakakou
We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminati…
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
Ethan Dack, Lorenzo Brigato, Matthew McMurray +9
The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations. Previous research on automated diagnosis of COV…
No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets
Lorenzo Brigato, Stavroula Mougiakakou
Solving image classification tasks given small training datasets remains an open challenge for modern computer vision. Aggressive data augmentation and generative models are among…
Food Recognition and Nutritional Apps
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato +2
Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet. Such apps can monitor food intake as a digita…
Partially Supervised Multi-Task Network for Single-View Dietary Assessment
Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou
Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods bas…
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou +3
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutr…
Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries
Matthias Fontanellaz, Stergios Christodoulidis, Stavroula Mougiakakou
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 vari…
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients
Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou +6
Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate n…
Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy
Qingnan Sun, Marko V. Jankovic, Stavroula G. Mougiakakou
The existing adaptive basal-bolus advisor (ABBA) was further developed to benefit patients under insulin therapy with multiple daily injections (MDI). Three different in silico exp…
A dual mode adaptive basal-bolus advisor based on reinforcement learning
Qingnan Sun, Marko V. Jankovic, João Budzinski +4
Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed…
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou +4
Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a n…
Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network
Qingnan Sun, Marko V. Jankovic, Lia Bally +1
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A seq…
A Multi-Task Learning Approach for Meal Assessment
Ya Lu, Dario Allegra, Marios Anthimopoulos +3
Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming…
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Marios Anthimopoulos, Stergios Christodoulidis, Lukas Ebner +3
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnosti…
Two-view 3D Reconstruction for Food Volume Estimation
Joachim Dehais, Marios Anthimopoulos, Sergey Shevchik +1
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 accura…
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner +2
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In or…