AI predicts pediatric glioma recurrence using multiple brain scans

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To a…

Artificial intelligence tool helps predict relapse of pediatric brain cancer

Researchers trained and validated a deep learning model that can detect subtle changes across post-treatment brain scans and forecast glioma recurrence with up to 89 percent accuracy.

AI predicts pediatric glioma recurrence using multiple brain scans

Artificial intelligence (AI) shows tremendous promise for analyzing vast medical imaging datasets and identifying patterns that may be missed by human observers.

The chances of survival of children suffering from cancer have increased sharply, but some brain tumors remain difficult to treat. A new approach to therapy shows successes.

AI-assisted brain MRI shows promise for improved glioma care

An AI-based technique used to analyze brain MRI exams taken of children with gliomas shows promise for predicting recurrence.