Keywords: Diagnosis/Prediction, Tumor, Hemangioblastoma, Contrastive Learning
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.
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