Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify
the entire complement of proteins in cells or tissues. Here, we review challenges and recent …

The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

Molecular characterization of cell types using single-cell transcriptome sequencing is
revolutionizing cell biology and enabling new insights into the physiology of human organs …

Histopathology image evaluation is indispensable for cancer diagnoses and subtype
classification. Standard artificial intelligence methods for histopathology image analyses …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …

A central challenge in biology is obtaining high-content, high-resolution information while
analyzing tissue samples at volumes relevant to disease progression. We address this here …