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

arXiv: https://arxiv.org/abs/2111.00742 · HTML: https://mlsanity.com/paper/2111.00742/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)

## Built on by (most cited first, 15 of 15)

- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation](https://mlsanity.com/paper/2402.07008/) (2024)
- [ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation](https://mlsanity.com/paper/2508.01058/) (2025)
- [Train-Free Segmentation in MRI with Cubical Persistent Homology](https://mlsanity.com/paper/2401.01160/) (2024)
- [A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2412.06088/) (2024)
- [BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2308.16819/) (2023)
- [Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models](https://mlsanity.com/paper/2402.07354/) (2024)
- [Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection](https://mlsanity.com/paper/2408.14847/) (2024)
- [Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation](https://mlsanity.com/paper/2503.05682/) (2025)
- [MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation](https://mlsanity.com/paper/2510.26996/) (2025)
- [Explainable Deep Radiogenomic Molecular Imaging for MGMT Methylation Prediction in Glioblastoma](https://mlsanity.com/paper/2601.07035/) (2026)
- [Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment](https://mlsanity.com/paper/2602.21703/) (2026)
- [SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation](https://mlsanity.com/paper/2605.22572/) (2026)

## Abstract (arXiv)

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.