Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation

Abstract

We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.

arXiv commentAccepted at MICCAI 2019

References in corpus (3)

• Semi-Supervised Learning with Deep Generative Models
• Semi-Supervised Learning with Ladder Networks
• Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images

Cited by in corpus (5)

• Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
• Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy
• AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
• Exploring the parameter reusability of CNN
• Using UNet and PSPNet to explore the reusability principle of CNN parameters