As data-driven science, artificial intelligence (AI) has paved a promising path toward an
evolving health system teeming with thrilling opportunities for precision oncology …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

This study evaluates the quality of published research using artificial intelligence (AI) for
ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of …

Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles
hindering the application of deep learning models for nuclei analysis, which holds a broad …

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass
slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract …

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput
information from various sources, including medical images, pathological images, and …

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology
images. While supervised machine learning techniques necessitate the need for labels …

Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-
art performance in many medical image analysis tasks. Histopathological images contain …

Objective We have established 4 histopathologic subtyping of high-grade serous ovarian
cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse …