# Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2105.11486 · HTML: https://mlsanity.com/paper/2105.11486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 24 May 2021.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Data Distillation: Towards Omni-Supervised Learning](https://mlsanity.com/paper/1712.04440/) (2017)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Glioma Segmentation with Cascaded Unet](https://mlsanity.com/paper/1810.04008/) (2018)
- [Knowledge Distillation for Brain Tumor Segmentation](https://mlsanity.com/paper/2002.03688/) (2020)

## Abstract (arXiv)

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.