Digital pathology and microscopy image analysis is widely used for comprehensive studies
of cell morphology or tissue structure. Manual assessment is labor intensive and prone to …

Accurate segmentation of cervical cells in Pap smear images is an important step in
automatic pre-cancer identification in the uterine cervix. One of the major segmentation …

Deconvolution can be used to obtain sharp images or volumes from blurry or encoded
measurements in imaging systems. Given knowledge of the system's point spread function …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …

The visual attributes of cells, such as the nuclear morphology and chromatin openness, are
critical for histopathology image analysis. By learning cell-level visual representation, we …

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most
challenging tasks in automated cervix cytological analysis due to specifically the presence of …

Background Protein function in eukaryotic cells is often controlled in a cell cycle-dependent
manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a …

Detecting overlapping blob objects is a classical, yet challenging problem in the image
processing field. In this paper, we propose an automated blob detection method that is able …

One of the methods for stratifying different molecular classes of breast cancer is the
Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain …

Traditional miniaturized fluorescence microscopes are critical tools for modern biology.
Invariably, they struggle to simultaneously image with a high spatial resolution and a large …