Pathology articles from across Nature Portfolio

Borah et al. develop a rapid digital pathology approach for the analysis of fresh tissue specimens, avoiding the need for frozen section histopathology. They apply the method to fresh human brain specimens and demonstrate their ability to distinguish normal and tumor regions.

Federated learning enables multi-institutional collaborations on decentralized data with improved privacy protection. Here, authors propose a new scheme for decentralized federated learning with much less communication overhead and stronger privacy.

A decision-support system that automates the Banff classification system for diagnosis of kidney allograft rejection leads to reclassification and correction of pathologists’ diagnoses and improves risk stratification of allograft outcomes.

Artificial Intelligence (AI) has the potential of assisting the study and diagnosis of veterinary cancers. Here, the authors build a cancer digital pathology atlas encompassing multiple animal species and demonstrate an AI approach for comparative pathology, which yields insights about immune response and morphological similarities.

Krogue et al. develop a deep learning approach for the prediction of lymph node metastasis in patients with colorectal cancer. Computationally-extracted histological features of the primary tumor add predictive value to a set of clinicopathologic factors.