Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains ...

In recent years, AI has emerged as a powerful tool for analyzing medical images. Thanks to advances in computing and large medical datasets from which AI can learn, it has proven to be a valuable aid ...

Abstract: In the field of medical image analysis, medical image classification is one of the most fundamental and critical tasks. Current researches often rely on the off-the-shelf backbone networks ...

In healthcare and research environments, there's often a need to automatically separate medical images (X-rays, MRIs, CT scans, ultrasounds) from non-medical content (landscapes, objects, people) ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Toddlers may swiftly master the meaning of the word “no”, but many artificial intelligence models struggle to do so. They show a high fail rate when it comes to understanding commands that contain ...

Abstract: Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies ...

As emerging AI tools are being used in healthcare, doctors are spending more time looking at their patients instead of computer screens. Researchers are interpreting medical images faster and more ...