Accurate wind speed forecasting is essential for mitigating the challenges of wind power grid integration. However, existing wind speed prediction models overlook the distributional shift problem within wind speed series, and this time-varying distribution can significantly impact wind prediction accuracy. In this paper, we propose the Distribution Shift and Feature Decoupling Network (DSFD-Net), which addresses the issue of distributional shifts occurring both within the input series and between the input and predicted series through a distribution matching model and distribution mapping module, respectively. Additionally, we introduce a feature decoupling module to mitigate the feature loss encountered in our work. We conduct extensive experiments on two datasets, and comprehensive experimental results demonstrate that DSFD-Net achieves at least a 4.1% reduction in error metrics compared to other wind speed forecasting models, indicating superior performance.

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current research.Among various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis.

Refining the seed region to obtain finely annotated pseudo masks for training a segmentation model is a crucial step in the multi-stage weakly supervised semantic segmentation (WSSS) framework. One of the most popular refinement methods, IRN, extends seed regions towards the edges in the image. However, we observed that, due to the lack of guidance from semantic information, IRN’s refinement may lead the generation of partially erroneous refinement directions. To address this issue, we leverage prototypes to recover the overlooked category semantic information in the refinement stage. We propose a prototype-based pseudo mask reclassification post-processing (PtReCl) to correct misclassified pixels in the pseudo masks, generating refined pseudo masks with more accurate coverage. Experimental evaluations demonstrate that our post-processing approach brings improvements in both pseudo mask quality and segmentation results on PASCAL VOC and MS COCO datasets, achieving state-of-the-art performance on VOC.

The resolution of a whole slide image (WSI) is too large to process directly, but WSI can be segmented into patches and be classified through multiple instance learning (MIL). Some patches have either close distances or similar pathological morphology, indicating that there are at least two types of relationships between patches. However, the existing MIL methods often deal with instance relationships simplistically. To solve this problem, we propose a new model named DualGCN-MIL. It analyzes the two types of relationships in WSI by constructing two different graphs in high-dimensional feature space and physical coordinate space and uses graph convolutional networks with unshared parameters for learning. Various experiments conducted on the Camelyon dataset have shown that compared to state-of-the-art methods, we can achieve better performance in multiple indicators, with accuracy of 88.89% and 85.11% on the 16 and 17 datasets, respectively, which proves that DualGCN-MIL has a higher accuracy in WSI classification. Implementation can be obtained from https://github.com/UnmatchedKatana/DualGCN-MIL.

Semi-supervised domain generalization (SSDG) aims to build a domain-generalized model using partially labeled data from source domains. Mainstream SSDG methods follow the augmentation consistency in FixMatch. However, the extraction of domain-invariant features may be challenging due to the absence of feature-based operations, further leading to overfitting of the classifier. To this end, we propose Multi-level Augmentation Consistency Learning (MACMatch), which improves the generalization of feature extractor and classifier through feature-based augmentation consistency. On the other hand, existing methods assume labeled data are class-balanced and domain-balanced, which is easily violated in practice. Based on this, we introduce Representativity and Diversity-based Sample Selection (RDSS), which models data as graphs to evaluate reasonable samples for labeling, relaxing the assumption for labeled data. Experiments on PACS and OfficeHome demonstrate that MACMatch outperforms state-of-the-art SSDG methods. Furthermore, MAC-Match with RDSS achieves competitive results without domain and class priori assumptions. Code is available at https://github.com/Y-J-Zhang/MACMatch-RDSS.

Unsupervised Domain Adaptation (UDA) methods have become essential for computer-aided diagnostic analysis on medical images due to the advantage of improving the model generalization ability with fewer annotations. The disentanglement-based UDA methods improve the segmentation performance significantly by disentangling the features into domain-specific and domain-invariant components. However, such methods neglect detailed texture in ultrasound images, resulting in inaccurate segmentations of nodules. To address the above problem, we propose a Task-Oriented Feature Disentanglement (TOFD) method to achieve better UDA segmentation on images from different ultrasound machines. TOFD achieves finer-grained disentanglement by the Feature Constraint Module based on maximum entropy. Furthermore, the Domain Label Encoding Module can improve the stability of features by integrating the latent feature of domain bias. Extensive experiments demonstrate that TOFD can effectively enhance the model’s generalization capability and confirm the performance of TOFD on ultrasound images exceeds that of state-of-the-art UDA semantic segmentation.

The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. To alleviate this problem, data re-sampling and loss re-weighting techniques are often used to reshape the decision boundary of the classifier. However, these techniques still lead to biased decision boundary due to the lack of sufficient and diversified samples in the tail classes of medical image datasets. In this paper, we propose an implicit feature augmentation with feature transfer (FT-IFA) method which solves the class imbalance problem by expanding the feature space of tail classes to reshape the decision boundary of the classifier. FT-IFA utilizes prototype similarity to transfer the rich transformation information from the head classes to tail classes on the basis of the balanced feature space, enriching the intra-class diversity of tail classes. Experimental results on two class-imbalanced medical image datasets show that FT-IFA outperforms the current state-of-the-art methods and effectively solves the class imbalance problem.

The generation-based data augmentation method can overcome the challenge caused by the imbalance of medical image data to a certain extent. However, most of the current research focus on images with unified structure which are easy to learn. What is different is that ultrasound images are structurally inadequate, making it difficult for the structure to be captured by the generative network, resulting in the generated image lacks structural legitimacy. Therefore, a Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation is proposed in this paper, including a network and a strategy. Our Progressive Texture Generative Adversarial Network alleviates the adverse effect of completely truncating the reconstruction of structure and texture during the generation process and enhances the implicit association between structure and texture. The Image Data Augmentation Strategy based on Mask-Reconstruction overcomes data imbalance from a novel perspective, maintains the legitimacy of the structure in the generated data, as well as increases the diversity of disease data interpretably. The experiments prove the effectiveness of our method on data augmentation and image reconstruction on Structurally Inadequate Medical Image both qualitatively and quantitatively. Finally, the weakly supervised segmentation of the lesion is the additional contribution of our method.

Weakly supervised segmentation techniques based on medical images ease the reliance of models on pixel-level annotation while advancing the field of computer-aided diagnosis. However, the differences in nodule size in thyroid ultrasound images and the limitations of class activation maps in weakly supervised segmentation methods lead to under- and over-segmentation problems in prediction. To alleviate this problem, we propose a novel weakly supervised segmentation network. This method is based on a dual branch soft erase module that expands the foreground response region while constraining the erroneous expansion of the foreground region by the enhancement of background features. In addition, the sensitivity of the network to the nodule scale size is enhanced by the scale feature adaptation module, which in turn generates integral and high-quality segmentation masks. The results of experiments performed on the thyroid ultrasound image dataset showed that our model outperformed existing weakly supervised semantic segmentation methods with Jaccard and Dice coefficients of 50.1% and 64.5%, respectively.

With the maturity of artificial intelligence, AI-aided diagnosis technology is gradually widely applied in clinical medicine. However, for the same pathological tissue, medical images produced by different types of instruments usually possess different data distributions. Because of the domain shift phenomenon, AI-aided diagnosis cannot accurately diagnose medical images in other domains, which is a waste of precious medical images. This paper proposes a Multi-Scale Self-Attention Unsupervised Domain Adaptive framework (MSDAN), which consists of three modules. First, the multi-scale framework constrains the source domain features and target domain features by optimizing adversarial losses with different level features. Second, the mix-up discriminator extracts latent spatial features by mixing up source domain and target domain features. Finally, MSDAN learns the geometric information of the pathological tissues in medical images through the self-attention module, thereby improving the transfer effect of the semantic information in medical images. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity, especially for thyroid ultrasound images.

MRI technology is usually used to distinguish the grade of the tumor in the patient. Due to technical limitations, the classification of tumors (high-grade gliomas and metastases) on MRI images has become a problem for doctors. At present, the widely used neural network is gradually applied to the tumor classification of MRI images, which not only reduces the burden of human resources, but also shows good classification accuracy. Although different good experimental data are obtained under various neural networks, there is still a problem in using these neural networks for tumor classification: the semantic information expressed on the image by the deep features of the neural network is too scattered, and it is difficult to concentrate the lesion area. In this article, we propose a new strategy that combines the approximate symmetry properties of the MRI image with neural network, then uses a dual-branch network instead of the basic network for feature extraction, and adds complementary learning to the network, different features fusion and attention mechanism to enrich detailed information. Our method performs multiple comparison and ablation experiments on the dataset of glioma and metastasis, which proves that the proposed method is effective for tumor classification assisted by MRI.

Complicated medical image analysis often requires a combination of disease classification, lesion detection and lesion segmentation. However, models designed for different tasks produce inconsistent or non-corresponding predictions and ignore the implicit connections between tasks. We propose a novel framework, which makes full use of the fact that segmentation and detection are mutually beneficial, boosts these three tasks in a unified framework. The proposed Information Enhancement Module uses classification information as a beneficial supplement to locate lesion quickly for segmentation. To further achieve fine segmentation with clear boundaries, we propose a Boundary-aware Loss, which dynamically adjusts supervised signal, so that our model pays more attention to boundary in later stages of training. Through experiments conducted on Thyroid Ultrasound dataset, we have demonstrated the good performance of the proposed method in joint segmentation and detection.

Thyroid ultrasound image is an important basis for artificial intelligence assisted treatment of thyroid-related diseases, but existing images usually contain special cross symbols which represent the location of nodules marked by doctors, thus affecting the features and diagnostic results extracted by the deep learning algorithm. We propose Pyramid GAN(Py-GAN) for blind image inpainting to remove cross symbols. Py-GAN contains a generator with pyramid structure and a global discriminator. The global discriminator improves the authenticity of the corrupted regions and image consistency. The generator uses the joint context loss to get clear image restoration, which prevents the information loss of non-completion area. The inpainting results of the proposed Py-GAN not only maintains the texture and structural information of the original image, but also has the greatest advantage that there are no artifacts in the corrupted regions, achieving pixel-level realism. Both qualitative and quantitative comparisons are superior to existing learning/non-learning image inpainting works.

Image segmentation based on deep learning has greatly promoted the development of the field of computer-aided diagnosis. However, the large scale medical annotation of ground truth is so difficult that it directly affects the performance of existing segmentation models. In this work, an Attention based Semi-supervised Neural Network is proposed, which can complete end-to-end segmentation task of thyroid ultrasound image with weakly annotated classification data and a small amount of fully annotated segmentation data. Two kinds of attention modules are proposed to improve network performance through the trainable feedforward structure of bottom-up and top-down so as to suppress or activate the feature channels and image regions respectively. The experimental results show that when there is only 13% of fully annotated data, the Jaccard similarity coefficient of thyroid nodule segmentation is 74.91%, 4.97% higher than VGG-based semi-supervised model. The classification accuracy of benign and malignant is increased from 91.67% to 95.00%. Equally important, with the same number of fully annotated data, our model has better generalization than that of the supervised segmentation models.