# Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs

Source: [Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs](<https://www.sota2.com/research/paper/68fa6b2f1e209131cea2cae6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:42:15.448Z

Authors: Md Mahfuzur Rahman Siddiquee, Andriy Myronenko

Published: 2021-11-01T07:39:06.000Z

## Abstract

Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturbations. Given a set trained networks, we further introduce a confidence based ensembling techniques to further improve the performance. We evaluated the method on BraTS 2021 validation board, and achieved 0.8600, 0.8868 and 0.9265 average dice for enhanced tumor core, tumor core and whole tumor, respectively. Our team (NVAUTO) submission was the top performing in terms of ET and TC scores and within top 10 performing teams in terms of WT scores.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.00742>)

[PDF](<https://arxiv.org/pdf/2111.00742.pdf>)