Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the
dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in …

This systematic review analyses and describes the application and diagnostic accuracy of
Artificial Intelligence (AI) methods used for detection and grading of potentially malignant …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

Remarkable progress in molecular analyses has improved our understanding of the
evolution of cancer cells toward immune escape,,,–. However, the spatial configurations of …

We study the semi-supervised learning problem, using a few labeled data and a large
amount of unlabeled data to train the network, by developing a cross-patch dense …

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 …

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …