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

TL;DR

This paper introduces a semi-supervised deep learning framework using ensemble convolutional neural networks to improve medical image segmentation performance with limited labeled data.

Contribution

It presents a novel ensemble-based semi-supervised learning approach that iteratively refines models using pseudo labels, reducing the need for extensive annotated datasets.

Findings

Significant performance improvement over fully supervised models

Effective utilization of unlabeled data in medical image segmentation

Iterative model refinement enhances segmentation accuracy

Abstract

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.