Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 62,751 to 62,760 of 230,507 articles

Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening

arXiv
Early identification of stroke symptoms is essential for enabling timely intervention and improving patient outcomes, particularly in prehospital settings. This study presents a fast, non-invasive multimodal deep learning framework for automatic bina... read more 

Task-tailored Pre-processing: Fair Downstream Supervised Learning

arXiv
Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categor... read more 

RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection

arXiv
Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster assessment. Meanwhile, visual autoregressive models (VARs) have recen... read more 

LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning

arXiv
We introduce a unified framework that seamlessly integrates algorithmic recourse, contextual bandits, and large language models (LLMs) to support sequential decision-making in high-stakes settings such as personalized medicine. We first introduce the... read more 

Towards Airborne Object Detection: A Deep Learning Analysis

arXiv
The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited ... read more 

Reliable Deep Learning for Small-Scale Classifications: Experiments on Real-World Image Datasets from Bangladesh

arXiv
Convolutional neural networks (CNNs) have achieved state-of-the-art performance in image recognition tasks but often involve complex architectures that may overfit on small datasets. In this study, we evaluate a compact CNN across five publicly avail... read more 

Effects of Gabor Filters on Classification Performance of CNNs Trained on a Limited Number of Conditions

arXiv
In this study, we propose a technique to improve the accuracy and reduce the size of convolutional neural networks (CNNs) running on edge devices for real-world robot vision applications. CNNs running on edge devices must have a small architecture, a... read more 

SupScene: Learning Overlap-Aware Global Descriptor for Unconstrained SfM

arXiv
Image retrieval is a critical step for alleviating the quadratic complexity of image matching in unconstrained Structure-from-Motion (SfM). However, in this context, image retrieval typically focuses more on the image pairs of geometric matchability ... read more 

Language-Guided and Motion-Aware Gait Representation for Generalizable Recognition

arXiv
Gait recognition is emerging as a promising technology and an innovative field within computer vision. However, existing methods typically rely on complex architectures to directly extract features from images and apply pooling operations to obtain s... read more 

Deep learning-based neurodevelopmental assessment in preterm infants

arXiv
Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for timely intervention. While deep learning-based volumetric segmentation of brain MRI scans offers a p... read more