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

Traditional pathology approaches have played an integral role in the delivery of diagnosis,
semi-quantitative or qualitative assessment of protein expression, and classification of …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Simple Summary Diagnosing cancer at an early stage increases the chance of performing
effective treatment in many tumour groups. Key approaches include screening patients who …

Background Multiple studies have compared the performance of artificial intelligence (AI)–
based models for automated skin cancer classification to human experts, thus setting the …

The rapidly emerging field of computational pathology has demonstrated promise in
developing objective prognostic models from histology images. However, most prognostic …

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 …

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic
diagnosis of cancer is increasing as personalized cancer therapy requires accurate …

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the
same drugs or surgical methods in patients with the same tumor may have different curative …

Purpose: Several biomarkers of response to immune checkpoint inhibitors (ICI) show
potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep …