A Computer-aided Diagnoses Method for Brainstem-and-cerebellar Hemangioblastoma

Motivation: Brainstem and cerebellar hemangioblastoma is a rare tumor with a high risk of haemorrhage during the biopsy. However, it is still a challenging task to distinguish HB from other types of intracranial tumors solely based on neuroimaging techniques. Goal(s): To propose a computer-aided diagnosis method to classify hemangioblastoma. Approach: We propose a patient-level classification framework using multi-task supervised contrastive learning, named LaSCL-PLC, for hemangioblastoma classification. Results: We evaluated the proposed model on a local MRI dataset of brainstem-and-cerebellum tumors, consisting of 97(HB) and 143(others). The experimental results show that our model achieves competitive performance as neuroradiologists. Impact: It could improve the preoperative diagnosis hemangioblastoma, which is crucial for clinical treatments.