To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …

In the past decade, deep neural networks, particularly convolutional neural networks, have
revolutionised computer vision. However, all deep learning models may require a large …

Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Motivation Medical image analysis involves a series of tasks used to assist physicians in
qualitative and quantitative analyses of lesions or anatomical structures which can …

Pre-processing is widely applied in medical image analysis to remove the interference
information. However, the existing pre-processing solutions mainly encounter two …

The development of omics technologies has driven a profound expansion in the scale of
biological data and the increased complexity in internal dimensions, prompting the …

Artificial intelligence (AI) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …