# A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1812.03945 · HTML: https://mlsanity.com/paper/1812.03945/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 13 of 13)

- [DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization](https://mlsanity.com/paper/2209.05227/) (2022)
- [MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation](https://mlsanity.com/paper/2205.04846/) (2022)
- [Test-time generative augmentation for medical image segmentation](https://mlsanity.com/paper/2406.17608/) (2024)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation](https://mlsanity.com/paper/2210.01485/) (2022)
- [Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation](https://mlsanity.com/paper/2403.12767/) (2024)
- [Two layer Ensemble of Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2104.04809/) (2021)
- [Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving](https://mlsanity.com/paper/2105.07930/) (2021)
- [Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy](https://mlsanity.com/paper/2104.01985/) (2021)
- [Transductive image segmentation: Self-training and effect of uncertainty estimation](https://mlsanity.com/paper/2107.08964/) (2021)
- [Auxiliary Class Based Multiple Choice Learning](https://mlsanity.com/paper/2108.02949/) (2021)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning](https://mlsanity.com/paper/2312.01682/) (2023)

## Abstract (arXiv)

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.