Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Computational pathology refers to applying deep learning techniques and algorithms to
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …

Digital pathology has seen a proliferation of deep learning models in recent years, but many
models are not readily reusable. To address this challenge, we developed WSInfer: an open …

Until recently, conventional biochemical staining had the undisputed status as well-
established benchmark for most biomedical problems related to clinical diagnostics …

Introduction: Hematologists analyze microscopic images of red blood cells to study their
morphology and functionality, detect disorders and search for drugs. However, accurate …

Purpose: The spatial variability and clinical relevance of the tumor immune
microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). In …

Abstract Background and Objective: The classification of bone marrow (BM) cells by light
microscopy is an important cornerstone of hematological diagnosis, performed thousands of …

The large volume and variety of imaging data from high-throughput screening (HTS) in the
pharmaceutical industry present an excellent resource for training virtual staining models …

Immuno-oncology holds promise for transforming patient care having achieved durable
clinical response rates across a variety of advanced and metastatic cancers. Despite these …

Abstract Background and Objective Classification of peripheral blood and bone marrow cells
is critical in the diagnosis and monitoring of hematological disorders. The development of …