Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating
mortality rates that have not changed over the past decades, in the face of advancements in …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …

Artificial intelligence methods including deep neural networks (DNN) can provide rapid
molecular classification of tumors from routine histology with accuracy that matches or …

Gene expression-based recurrence assays are strongly recommended to guide the use of
chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing …

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 …

Artificial intelligence models have been increasingly used in the analysis of tumor histology
to perform tasks ranging from routine classification to identification of molecular features …

The diagnosis of early-stage mycosis fungoides (MF) is challenging owing to shared clinical
and histopathological features with benign inflammatory dermatoses. Recent evidence has …

The introduction of machine learning in digital pathology has deeply impacted the field,
especially with the advent of whole slide image (WSI) analysis. In this review, we tried to …

A deep learning model using attention-based multiple instance learning (aMIL) and self-
supervised learning (SSL) was developed to perform pathologic classification of …

Background Deployment and access to state-of-the-art precision medicine technologies
remains a fundamental challenge in providing equitable global cancer care in low-resource …