Modern drug discovery approaches often use high-content imaging to systematically study
the effect on cells of large libraries of chemical compounds. By automatically screening …

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO)
framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell …

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …

The growth of artificial intelligence (AI) has led to an increase in the adoption of computer
vision and deep learning (DL) techniques for the evaluation of microscopy images and …

Spatial transcriptomics promises to greatly improve our understanding of tissue organization
and cell–cell interactions. While most current platforms for spatial transcriptomics only offer …

Accurate cell-type annotation from spatially resolved single cells is crucial to understand
functional spatial biology that is the basis of tissue organization. However, current …

Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …

The detection and segmentation of individual cells or nuclei is often involved in image
analysis across a variety of biology and biomedical applications as an indispensable …

Abnormalities in biological cell nuclei shapes are correlated with cell cycle stages, disease
states, and various external stimuli. There have been many deep learning approaches that …

Histopathological images are widely used for the analysis of diseased (tumor) tissues and
patient treatment selection. While the majority of microscopy image processing was …