In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains
challenging, due to cost, expertise, methodical constraints, and access to technology. An …

Spatial transcriptomics is a rapidly growing field that promises to comprehensively
characterize tissue organization and architecture at the single-cell or subcellular resolution …

Advances in computational algorithms and tools have made the prediction of cancer patient
outcomes using computational pathology feasible. However, predicting clinical outcomes …

The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …

In pathology and biomedical research, histology is the cornerstone method for tissue
analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time …

Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …