# A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

arXiv: https://arxiv.org/abs/2004.07995 · HTML: https://mlsanity.com/paper/2004.07995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.12571/) (2022)
- [A Unified Framework for Semi-Supervised Image Segmentation and Registration](https://mlsanity.com/paper/2502.03229/) (2025)
- [Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy](https://mlsanity.com/paper/2411.14752/) (2024)

## Abstract (arXiv)

Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.