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

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

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 …

Computational methods have made substantial progress in improving the accuracy and
throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still …

Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …

The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing
additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic …

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment
selection and planning. With the rapid advance of medical imaging technology, whole slide …

Lung cancer is a disease caused by the involuntary increase of cells in the lung tissue. Early
detection of cancerous cells is of vital importance in the lungs providing oxygen to the …