# Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

arXiv: https://arxiv.org/abs/2104.00985 · HTML: https://mlsanity.com/paper/2104.00985/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [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)
- [Real-Time Instrument Segmentation in Robotic Surgery using Auxiliary Supervised Deep Adversarial Learning](https://mlsanity.com/paper/2007.11319/) (2020)
- [Learning Where to Look While Tracking Instruments in Robot-assisted Surgery](https://mlsanity.com/paper/1907.00214/) (2019)

## Built on by (most cited first, 19 of 19)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.05274/) (2021)
- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images](https://mlsanity.com/paper/2307.01124/) (2023)
- [Generative Adversarial Networks for Brain Images Synthesis: A Review](https://mlsanity.com/paper/2305.15421/) (2023)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2305.12358/) (2023)
- [Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping](https://mlsanity.com/paper/2203.04299/) (2022)
- [Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net](https://mlsanity.com/paper/2305.06203/) (2023)
- [Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation](https://mlsanity.com/paper/2502.00563/) (2025)
- [Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decoding](https://mlsanity.com/paper/2509.05431/) (2025)
- [Autoregressive Video Autoencoder with Decoupled Temporal and Spatial Context](https://mlsanity.com/paper/2512.11293/) (2025)
- [UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion](https://mlsanity.com/paper/2505.03494/) (2025)
- [An Efficient Model-Driven Groupwise Approach for Atlas Construction](https://mlsanity.com/paper/2508.10743/) (2025)
- [No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data](https://mlsanity.com/paper/2509.11406/) (2025)
- [AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification](https://mlsanity.com/paper/2510.00882/) (2025)
- [Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework](https://mlsanity.com/paper/2510.17039/) (2025)
- [A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries](https://mlsanity.com/paper/2607.11672/) (2026)

## Abstract (arXiv)

In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments to show the effect of each feature for overall survival (OS) prediction. The experimental results infer that radiomic features such as histogram, location, and shape of the necrosis region and clinical features like age are the most critical parameters to estimate the OS.