# Two layer Ensemble of Deep Learning Models for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2104.04809 · HTML: https://mlsanity.com/paper/2104.04809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 82 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [The Marginal Value of Adaptive Gradient Methods in Machine Learning](https://mlsanity.com/paper/1705.08292/) (2017)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [Deep supervision with additional labels for retinal vessel segmentation task](https://mlsanity.com/paper/1806.02132/) (2018)
- [Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation](https://mlsanity.com/paper/1909.04542/) (2019)
- [A New Ensemble Learning Framework for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1812.03945/) (2018)
- [Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans](https://mlsanity.com/paper/1712.03993/) (2017)
- [Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1712.00542/) (2017)
- [BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation](https://mlsanity.com/paper/2102.08990/) (2021)

## Built on by (most cited first, 2 of 2)

- [YOLO-Based Pipeline Monitoring in Challenging Visual Environments](https://mlsanity.com/paper/2507.02967/) (2025)
- [Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation](https://mlsanity.com/paper/2608.05815/) (2026)

## Abstract (arXiv)

In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further improve the performance in the segmentation task, we develop an ensemble system which combines various deep learning architectures. We propose a two-layer ensemble of deep learning models for the segmentation of medical images. The prediction for each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weights-based scheme in which each model contributes differently to the combined result. The weights are found by solving linear regression problems. Experiments conducted on two popular medical datasets namely CAMUS and Kvasir-SEG show that the proposed method achieves better results concerning two performance metrics (Dice Coefficient and Hausdorff distance) compared to some well-known benchmark algorithms.