Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation

TL;DR

This paper explores the application of Knowledge Distillation techniques to improve brain tumor segmentation in multi-modal MRI scans, aiming to enhance diagnostic accuracy in medical imaging.

Contribution

It introduces the use of Knowledge Distillation methods specifically for brain tumor segmentation, which is a novel approach in this medical imaging context.

Findings

Knowledge Distillation improves segmentation accuracy.

Different distillation approaches are compared.

Enhanced model performance on MRI datasets.

Abstract

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.