In semi-supervised medical image segmentation, appropriately merging labeled and unlabeled data before network training instead of using them separately can effectively reduce knowledge loss, mitigate distribution discrepancies and promote efficient knowledge transfer to unlabeled data. However, existing methods tend to focus on data fusion, overlooking the importance of self-transformations. Therefore, we propose a comprehensive data augmentation strategy that combines self-transformations with cross-sample fusion: Symmetry and Fusion Data Augmentation (SF-DA), implemented within the Mean Teacher framework. Our method has two branches: Self-Symmetric Flipping (SSF), enhancing the model’s feature understanding, and cross-sample stitching (CSS), promoting common semantic learning between labeled and unlabeled data. Together, they promote more comprehensive knowledge transfer. Extensive experiments on ACDC and PROMISE12 datasets demonstrate the effectiveness and superiority of SF-DA. Across different labeled data scenarios, SF-DA consistently outperforms the second-best method in all evaluation metrics. Code is available at https://github.com/ZYS-four/SF-DA.git.

In this paper, we propose a novel and efficient method for knowledge distillation, which is structurally simple and requires negligible computation overhead. Our method includes three modules. The first module is the calibrated mask, which avoids the teacher model’s incorrect representation to disturb the student model’s training; the second module and the third module improve the performance of the student model by the similarity of the sample and the process, respectively. The student model attains better performance in qualitative and quantitative evaluation through the judicious amalgamation of these three modules. Our method is experimented with through rigorous validation of canonical datasets, including CIFAR-100 and TinyImageNet. The experimental corroboration conclusively attests to the better performance of our method, soaring above the extant most state-of-the-art on both subjective and objective dimensions.

Medical image segmentation plays a crucial role in medical imaging, especially with advancements in techniques like magnetic resonance imaging (MRI) and computed tomography (CT). UNet, a widely used architecture, has shown promising results in medical image segmentation. Several variants based on UNet, and Transformer-based models, like TransUNet, have also exhibited potential for improving segmentation performance. However, these models often require substantial data and computational resources, making them less suitable for on-the-fly segmentation in medical scenarios. This paper proposes a fast medical image segmentation network called R2L-Net, which leverages self-supervised and supervised learning. R2L-Net introduces a self-supervised relative localization task as a regularization term during network training to enhance performance. Compared to UNeXt, our proposed R2L-Net achieves superior results on two public datasets (ISIC and BUSI), with an improved Intersection over Union (IoU) by 5.22 and 5.36, respectively. Moreover, R2L-Net offers several advantages over existing models, including a small number of parameters, low computational complexity, and fast image processing.

Image inpainting has made huge strides benefiting from the advantages of convolutional neural networks (CNNs) in understanding high-level semantics. Recently, some studies have applied transformers to the visual field to solve the problem that the convolution kernel cannot attend to longdistance information. However, unlike other vision tasks, there is much interference from damaged information in image inpainting tasks. We propose a new Semantic-Aware Transformer, which in addition to including a self-attention block like previous vision transformers, also has a block for learning semantics from QSVM. Specifically, to provide more valid information, we design a Quantized Semantic Vector Memory (QSVM) that encodes and saves semantic features in images as quantized vectors in latent space. Experiments on different datasets demonstrate the effectiveness and superiority of our method compared with the existing state-of-the-art.

Image inpainting has made significant progress benefiting from the advantages of convolutional neural networks (CNNs). Deep learning-based methods have shown extraordinary performance in this field. In this paper, we propose a novel image inpainting architecture with pure CNN that can jointly reconstruct the structure and texture of the image. Our generative network architecture (TSFC) consists of two parallel stages: structure generation and texture generation. In the structure generation stage, we use the large convolution kernel, which is highly neglected in modern networks, using the effective perceptual field of the large convolution kernel to enhance the perception of overall structural features. In the texture generation stage, we use the small convolution kernel to extract local texture features. Qualitative and quantitative experimental results on CelebA-HQ and Paris Street View datasets demonstrate the effectiveness and superiority of our method.

In this paper, we propose Block-Correlation Knowledge Distillation (BCKD), a novel and efficient knowledge distillation method that differs from the classical method, using the simple multilayer-perceptron (MLP) and the classifier of the pre-trained teacher to train the correlations between adjacent blocks of the model. Over the past few years, the performance of some methods has been restricted by the feature map size or the lack of samples in small-scale datasets. By our proposed BCKD, the above problem is satisfactorily solved and has a superior performance without introducing additional overhead. Our method is validated on CIFAR100 and CI-FAR10 datasets, and experimental results demonstrate the effectiveness and superiority of our method.