Search Results for author:
Found 11 papers, 1 papers with code
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis
Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts.
Letter to the Editor: What are the legal and ethical considerations of submitting radiology reports to ChatGPT?
This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports.
A self-supervised text-vision framework for automated brain abnormality detection
To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports.
Reinforcement Learning (RL) Augmented Cold Start Frequency Reduction in Serverless Computing
It features serverless attributes by eliminating resource management responsibilities from developers and offers transparent and on-demand scalability of applications.
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions
Therefore, in this paper, we investigate a model-free Recurrent RL agent for function autoscaling and compare it against the model-free Proximal Policy Optimisation (PPO) algorithm.
Investigations on convergence behaviour of Physics Informed Neural Networks across spectral ranges and derivative orders
An important inference from Neural Tangent Kernel (NTK) theory is the existence of spectral bias (SB), that is, low frequency components of the target function of a fully connected Artificial Neural Network (ANN) being learnt significantly faster than the higher frequencies during training.
Automated triaging of head MRI examinations using convolutional neural networks
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world.
S-BEV: Semantic Birds-Eye View Representation for Weather and Lighting Invariant 3-DoF Localization
We describe a light-weight, weather and lighting invariant, Semantic Bird's Eye View (S-BEV) signature for vision-based vehicle re-localization.
Enhanced Normalized Mutual Information for Localization in Noisy Environments
Fine localization is a crucial task for autonomous vehicles.
Aerial Imagery based LIDAR Localization for Autonomous Vehicles
This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments.
Ford Multi-AV Seasonal Dataset
This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18.
