Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

Data-driven approaches have great potential to shape future practices in radiology. The
most straightforward strategy to obtain clinically accurate models is to use large, well …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

Although breast ultrasonography is the mainstay modality for differentiating between benign
and malignant breast masses, it has intrinsic problems with false positives and substantial …

We propose a fully automated algorithm based on a deep learning framework enabling
screening of a coronary computed tomography angiography (CCTA) examination for …

In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and
myocardium are performed to quantify LV ejection fraction and LV mass, and they are …

Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of
blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP) …

Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with
suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of …

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis
of the tumour region is critical for the doctors. But with data accumulation, this task takes lots …