Over the past decade, advancements in machine learning (ML) and deep learning (DL) have revolutionized segmentation accuracy.

Neural networks trained with a camouflage detection step show enhanced accuracy and sensitivity in identifying brain tumors from MRI scans, mimicking expert radiologists. Study: Deep learning and ...

A new liquid biopsy approach developed by Johns Hopkins Kimmel Cancer Center investigators could revolutionize brain cancer detection by identifying circulating DNA fragments from tumors and ...

Researchers propose an innovative deep learning model for accurately predicting MSI tumor and immune checkpoint inhibitor responsiveness.

A novel tool for rapidly identifying the genetic "fingerprints" of cancer cells may enable future surgeons to more accurately remove brain tumors while a patient is in the operating room, new ...

MANILA, Philippines — The Philippine General Hospital (PGH) is studying the viability of using Taiwanese artificial intelligence (AI) software that can detect brain tumors in five minutes. PGH ...

Tumor features were compared between paired primary tumors and their synchronous CRLMs using the Wilcoxon signed-rank test overall and within subgroups. Linear regression models were used to find ...

New AI model demonstrates high accuracy for predicting immune checkpoint inhibitor (ICI) responsiveness by integrating tumor MSI status with stroma-to-tumor ratio Cancer remains one of the most ...