Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …

Deep learning (DL) approaches to medical image analysis tasks have recently become
popular; however, they suffer from a lack of human interpretability critical for both increasing …

Most of the current state-of-the-art methods for tumor segmentation are based on machine
learning models trained manually on segmented images. This type of training data is …

Kymographs are graphical representations of spatial position over time, which are often
used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or …

Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation
in histology images is challenging in variable conditions (clinical wild), such as poor staining …

Since segmentation labeling is usually time-consuming and annotating medical images
requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated …

Artificial intelligence (AI) will continue to cause substantial changes within the field of
radiology, and it will become increasingly important for clinicians to be familiar with several …