A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
This addresses the time-consuming data annotation issue in medical imaging, offering a domain-specific incremental improvement for semi-supervised segmentation.
The paper tackles the problem of requiring large labeled datasets for medical image segmentation by proposing a semi-supervised learning framework that uses an ensemble of deep convolutional neural networks with pseudo-labels from unlabeled data, achieving significant improvement over fully supervised models on a skin lesion segmentation dataset.
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.