Drug development is a complex and time-consuming endeavor that traditionally relies on the
experience of drug developers and trial-and-error experimentation. The advent of artificial …

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This
comprehensive review examines the current applications and future prospects of AI in lung …

Rapid and precise cancer subtype discrimination is essential for personalized oncology.
Conventional diagnostic methods often lack sufficient accuracy and speed. Here, we …

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology
features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We …

Purpose: Necrosis quantification in the neoadjuvant setting using pathology slide review is
the most important validated prognostic marker in conventional osteosarcoma. Herein, we …

Precise survival risk stratification is crucial for personalized therapy in bladder cancer (BCa).
This study developed and validated an end-to-end deep learning system using histological …

Background This study explores the potential of the deep learning-based convolutional
neural network (CNN) to automatically recognize MMD using MRA images from …

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that
is considered societally responsible, particularly in cancer diagnostics, where a …

Histopathological assessment is the corner stone for tumor diagnosis in the clinic, but it is in
its optimal form (using paraffin-embedded sections) not readily available during surgery. The …

Mesothelioma is a highly lethal and poorly biologically understood disease which presents
diagnostic challenges due to its morphological complexity. This study uses self-supervised …