8 citations · 14 across the 10 of their papers we have counts for
10 papers
Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Ethan Dack, Lorenzo Brigato, Vasilis Dedousis +9
Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is part…
Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment
Maria Panagiotou, Lorenzo Brigato, Vivien Streit +13
Despite recent advances in insulin preparations and technology, adjusting insulin remains an ongoing challenge for the majority of people with type 1 diabetes (T1D) and longstandin…
The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes
Maria Panagiotou, Knut Stroemmen, Lorenzo Brigato +2
The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have advanced significantly, with artific…
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato +1
Food recognition models often struggle to distinguish between seen and unseen samples, frequently misclassifying samples from unseen categories by assigning them an in-distribution…
There are no Champions in Supervised Long-Term Time Series Forecasting
Lorenzo Brigato, Rafael Morand, Knut Strømmen +3
Recent advances in long-term time series forecasting have introduced numerous complex supervised prediction models that consistently outperform previously published architectures.…
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…