Multi-decoder Networks for Semi-supervised Medical Image Segmentation

Abstract

To improve the performance of semi-supervised image segmentation, it is important to effectively generate pseudo-labels from unlabeled images. However, the impact of pseudo-label confidence on segmentation performance is often overlooked. Low-confidence pseudo-labels can misguide the model and lead to overfitting, making it challenging to use them effectively. To address this issue, we propose a consistency constraint-based network that employs one encoder and three decoders () to generate distinct pseudo-labels. To assess the confidence of the generated pseudo-labels, we introduce a critic network that learns relevant features and effectively regularizes the confidence of -generated pseudo-labels. For evaluating the unlabeled images, we define a loss function that minimizes entropy, consisting of three sets of losses. We compare the performance of our model with two other semi-supervised segmentation algorithms using Dice, MAE, and F1 indicators. Our results demonstrate that the model outperforms the comparison models on all three metrics. In summary, our proposed consistency constraint-based network with a critic network and entropy-based loss function can effectively generate high-confidence pseudo-labels for semi-supervised image segmentation and improve the overall performance of the model.