Lung cancer is a leading contributor to cancer-related fatalities worldwide, and
histopathological image analysis plays a critical role in cancer detection by identifying …

Machine learning is increasingly significant in health science because it can infer valuable
information from high-dimensional data. However, combining research and patient data from …

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances
disease diagnosis using diverse medical datasets. However, learning generalizable …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

Background Multicenter non-small cell lung cancer (NSCLC) patient data is information-rich.
However, its direct integration becomes exceptionally challenging due to constraints …

Federated Learning (FL) could solve the challenges of training AI models on large datasets
for medical imaging due to data privacy and ownership concerns by allowing collaborative …

Lung cancer is a disease that affects and causes abnormalities in the lungs. The current
methods to find and treat lung cancer require precise and timely detection to improve patient …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Due to the rapid advancements in recent years, medical image analysis is largely dominated
by deep learning (DL). However, building powerful and robust DL models requires training …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …