Semi-supervised Medical Image Segmentation through Dual-task Consistency

Authors

  • Xiangde Luo University of Electronic Science and Technology of China SenseTime Research
  • Jieneng Chen Tongji University
  • Tao Song SenseTime Research
  • Guotai Wang University of Electronic Science and Technology of China

Keywords:

Semi-Supervised Learning, Healthcare, Medicine & Wellness, Segmentation

Abstract

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. However, most of the existing SSL algorithms in literature tend to regularize the model training by perturbing networks and/or data. Observing that multi/dual-task learning attends to various levels of information which have inherent prediction perturbation, we ask the question in this work: can we explicitly build task-level regularization rather than implicitly constructing networks- and/or data-level perturbation and then regularization for SSL? To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time. Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target. The level set representation is converted to an approximated segmentation map through a differentiable task transform layer. Simultaneously, we introduce a dual-task consistency regularization between the level set-derived segmentation maps and directly predicted segmentation maps for both labeled and unlabeled data. Extensive experiments on two public datasets show that our method can largely improve the performance by incorporating the unlabeled data. Meanwhile, our framework outperforms the state-of-the-art semi-supervised learning methods.

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Published

2021-05-18

How to Cite

Luo, X., Chen, J., Song, T., & Wang, G. (2021). Semi-supervised Medical Image Segmentation through Dual-task Consistency. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8801-8809. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17066

Issue

Section

AAAI Technical Track on Machine Learning III