Latest AI and machine learning research in pneumonia for healthcare professionals.
Modern single-cell datasets now comprise hundreds of millions of cells, presenting significant challenges for training deep learning models that require shuffled, memory-efficient data loading. While the AnnData format is the community standard for storing single-cell datasets, existing data loading solutions for AnnData are often inadequate: some require loading all data into memory, others con...
Kwak'wala is an Indigenous language spoken in British Columbia, with a rich legacy of published documentation spanning more than a century, and an active community of speakers, teachers, and learners engaged in language revitalization. Over 11 volumes of the earliest texts created during the collaboration between Franz Boas and George Hunt have been scanned but remain unreadable by machines. Com...
The escalating demand for medical image interpretation underscores the critical need for advanced artificial intelligence solutions to enhance the e...
Individuals with mental illness face significant challenges in achieving health equity due to social and structural determinants, fragmented healthcar...
Detecting lung abnormalities via chest X-rays is challenging due to understated tissue variations often ignored by traditional methods. Augmentation t...
INTRODUCTION: Respiratory diseases like pneumonia, asthma, and COPD are major global health concerns, significantly impacting morbidity and mortality ...
This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to ident...
The COVID-19 pandemic has significantly strained healthcare systems, highlighting the need for early diagnosis to isolate positive cases and prevent t...
Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine l...
Automated radiology report generation from chest X-ray (CXR) images has the potential to improve clinical efficiency and reduce radiologists' worklo...
While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, ef...
Artificial intelligence (AI)-based chest X-ray (CXR) interpretation assistants have demonstrated significant progress and are increasingly being app...
General-purpose clinical natural language processing (NLP) tools are increasingly used for the automatic labeling of clinical reports. However, inde...
In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, ...
Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in...
Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perp...
The development of large-scale image-text pair datasets has significantly advanced self-supervised learning in Vision-Language Processing (VLP). How...
INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to othe...
Implementation of digital health systems in low-middle-income countries (LMICs) often fails due to a lack of evaluations that take into account infr...
Multimodal federated learning holds immense potential for collaboratively training models from multiple sources without sharing raw data, addressing...