Channel prior convolutional attention for medical image segmentation

Characteristics such as low contrast and significant organ shape variations are often exhibited in medical images. The improvement of segmentation performance in medical imaging is limited by the generally insufficient adaptive capabilities of existing attention mechanisms. An efficient Channel Prior Convolutional Attention (CPCA) method is proposed in this paper, supporting the dynamic distribution of attention weights in both channel and spatial dimensions. Spatial relationships are effectively extracted while preserving the channel prior by employing a multi-scale depth-wise convolutional module. The ability to focus on informative channels and important regions is possessed by CPCA. A segmentation network called CPCANet for medical image segmentation is proposed based on CPCA. CPCANet is validated on two publicly available datasets. Improved segmentation performance is achieved by CPCANet while requiring fewer computational resources through comparisons with state-of-the-art algorithms. Our code is publicly available at \url{https://github.com/Cuthbert-Huang/CPCANet}.

Complementary consistency semi-supervised learning for 3D left atrial image segmentation

A network based on complementary consistency training (CC-Net) is proposed for semi-supervised left atrial image segmentation in this paper. From the perspective of complementary information, CC-Net effectively utilizes unlabeled data and resolves the problem that semi-supervised segmentation algorithms currently in use have a limited capacity to extract information from unlabeled data. A primary model and two complementary auxiliary models are part of the complementary symmetric structure of the CC-Net. A complementary consistency training is formed by the inter-model perturbation between the primary model and the auxiliary models. The main model is better able to concentrate on the ambiguous region due to the complementary information provided by the two auxiliary models. Additionally, forcing consistency between the primary model and the auxiliary models makes it easier to obtain decision boundaries with little uncertainty. CC-Net was validated in the benchmark dataset of 2018 left atrial segmentation challenge, reaching Dice of 89.42% with 10% labeled data training and 91.14% with 20% labeled data training. By comparing with current state-of-the-art algorithms, CC-Net has the best segmentation performance and robustness. Our code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.

Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning

Safety guarantee is essential in many engineering implementations. Reinforcement learning provides a useful way to strengthen safety. However, reinforcement learning algorithms cannot completely guarantee safety over realistic operations. To address this issue, this work adopts control barrier functions over reinforcement learning, and proposes a compensated algorithm to completely maintain safety. Specifically, a sum-of-squares programming has been exploited to search for the optimal controller, and tune the learning hyperparameters simultaneously. Thus, the control actions are pledged to be always within the safe region. The effectiveness of proposed method is demonstrated via an inverted pendulum model. Compared to quadratic programming based reinforcement learning methods, our sum-of-squares programming based reinforcement learning has shown its superiority.