Image-Based Classification of Pediatric Posterior Fossa Tumors: An End-to-End Solution

This computational solution presents an innovative approach to classifying brain tumors located in the posterior fossa of children, utilizing anonymized magnetic resonance imaging (MRI) scans obtained prior to surgery. The process involves a comprehensive end-to-end pipeline that begins with advanced image processing techniques to enhance and prepare the scans, followed by precise tumor segmentation to isolate the affected areas. Subsequently, sophisticated classification models analyze these segmented images to identify the primary types of pediatric posterior fossa tumors. For cases identified as medulloblastoma—one of the most common subtypes—the system further distinguishes among its four major molecular subgroups, providing a probability score to indicate the confidence level of each classification and a visual heat map to highlight key features influencing the decision. This tool aims to support clinicians in achieving more accurate and timely diagnoses, potentially improving treatment planning and patient outcomes.