Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation
This work addresses the problem of diagnosing gliomas in medicine, but it appears incremental as it applies existing methods to a specific domain without clear novel contributions.
The paper tackles brain tumor segmentation in multimodal MRI scans by experimenting with Knowledge Distillation techniques to address data quantity and variability issues, but no concrete results or numbers are provided.
Multi-modal magnetic resonance imaging (MRI) is a crucial method for analyzing the human brain. It is usually used for diagnosing diseases and for making valuable decisions regarding the treatments - for instance, checking for gliomas in the human brain. With varying degrees of severity and detection, properly diagnosing gliomas is one of the most daunting and significant analysis tasks in modern-day medicine. Our primary focus is on working with different approaches to perform the segmentation of brain tumors in multimodal MRI scans. Now, the quantity, variability of the data used for training has always been considered to be crucial for developing excellent models. Hence, we also want to experiment with Knowledge Distillation techniques.