Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate new images. VAE typically assumes that data follows a Gaussian distribution, but real data may follow other distributions. This inconsistency between the assumption and the true distribution can affect the modeling and reconstruction capabilities of VAE, which makes it difficult for traditional models to accurately capture the true distribution. To address the aforementioned issues, we propose a Prior Distribution Guided Gaussian Mixture Variational Autoencoder(PDGM-VAE). Specifically, we construct a Gaussian Mixture Prior Learner (GMPL) to capture complex features of the data distribution, enabling the model to learn and obtain a Gaussian mixture distribution that is reasonable and close to the real data distributions, which is then used as the prior distribution in the network. Furthermore, we build a Semantic-Aware Module with Embedded Prior Distribution (SAMEPD), integrating data and label information to learn the distribution parameters, enabling the network to learn and utilize the semantic knowledge contained in the labels. During training, by approximating the posterior distribution to the prior distribution, we enhance the model’s modeling and reconstruction capabilities, improving the quality of generated images. We evaluated the image generation task on five public datasets, and based on the FID metric, our proposed method outperformed other VAE methods.

In clinical practice, obtaining a large amount of labeled CNV data is very difficult. Semi-supervised learning can effectively utilize a large amount of unlabeled CNV data. Since CNV has complex features such as blurred and unevenly distributed pixels on the edges, there are differences in the segmentation difficulty between pixels in the same image. Existing semi-supervised segmentation methods do not consider the segmentation difficulty of pixels, which will reduce the segmentation accuracy. To address this problem, we propose a dual difficulty-aware adaptive pseudo-label learning (D2APL) method for semi-supervised CNV segmentation. The proposed dual difficulty awareness includes segmentation difficulty perception of pixels in labeled and unlabeled data. For labeled data, we propose a classification confidence-guided difficulty perception method. For unlabeled data, we propose a model stability-guided difficulty perception method. Finally, we propose a difficulty-aware self-training method to dynamically adjust the threshold of pseudolabels according to the difficulty, thereby improving the utilization of difficult-to-segment pixels in unlabeled data. Experimental results show that our method outperforms the state-of-the-art method in CNV segmentation.

Video semantic role grounding has gained substantial interest from both the academic and industrial communities. While existing methods have demonstrated considerable performance improvements, the influence of noisy and intra-object proposals, referring to proposals with the same object label, has yet to be explored in video semantic role grounding. In this study, we propose a semantic-aware contrastive learning network with proposal suppression to enhance the accuracy of grounding referenced objects. To fully exploit the semantic information in each semantic role, we introduce a novel semantic role encoding module that allows for precise representations of each semantic role. We also design a semantic-aware proposal suppression network to reduce the impact of noisy proposals on object representation learning. Additionally, we propose a proposal contrastive loss to improve cross-modal alignment and reduce the effect of irrelevant intra-object proposals. Extensive experiments on four datasets demonstrate that our model achieves significant improvements over state-of-the-art methods.

Hashing can facilitate efficient retrieval and storage for large-scale images due to the binary representation. In the real applications, the trade-off between retrieval accuracy and speed is essential for designing a hashing framework, which is reflected by variable hash code lengths. In light of this, the existing hashing methods need to train different models for different lengths of hash codes, leading to considerable training time cost and hashing flexibility reduction. Given that a sample can be represented by various hash codes with different lengths, there are some helpful relationships that can boost the performance of hashing methods. However, the existing hashing methods do not fully utilize these relationships. To address the aforementioned issues, we propose a new model, known as supervised discrete multiple-length hashing (SDMLH), to simultaneously learn hash codes with multiple lengths. In this proposed SDMLH method, three types of information are respectively derived, from the hash codes with different lengths. The original features of the samples, and the label, are applied for hash learning. Unlike the existing hashing methods, SDMLH can fully employ the assistance among hash codes with different lengths and learn them in one step. Furthermore, given a hash length meeting the demand of users, we propose a hash fusion strategy to obtain the hash code with this desirable length by fusing the multiple-length hash codes. This obtained hash code outperforms the one learned directly. In addition, SDMLH can generate the hash code of any length that is shorter than the sum length of given multiple hash codes with the fusion strategy. To the best of our knowledge, SDMLH is one of the first attempts for learning multiple-length hash codes simultaneously. We conduct extensive experiments based on three benchmark datasets, demonstrating the superiority of this proposed method.

With the progress of clinical imaging innovation and machine learning, the computer-assisted diagnosis of breast histology images has attracted broad attention. Nonetheless, the use of computer-assisted diagnoses has been blocked due to the incomprehensibility of customary classification models. In view of this question, we propose a novel method for Learning Binary Semantic Embedding (LBSE). In this study, bit balance and uncorrela-tion constraints, double supervision, discrete optimization and asymmetric pairwise similarity are seamlessly integrated for learning binary semantic-preserving embedding. Moreover, a fusion-based strategy is carefully designed to handle the intractable problem of parameter setting, saving huge amounts of time for boundary tuning. Based on the above-mentioned proficient and effective embedding, classification and retrieval are simultaneously performed to give interpretable image-based deduction and model helped conclusions for breast histology images. Extensive experiments are conducted on three benchmark datasets to approve the predominance of LBSE in different situations.

In clinical practice, as a powerful and independent risk indicator of cardiovascular disease (CVD), accurate coronary artery calcium (CAC) segmentation can provide important information for the early diagnosis of CVD. However, due to the small and inconsistent CAC usually has fuzzy boundaries, which leads existing segmentation methods to suffer from unsatisfactory performance. To tackle this challenge, we propose a novel Efficient Multi-scale Vision Transformers for CAC segmentation (CAC-EMVT), which uses both the local and global branches to jointly model short- and long-range dependencies. CAC-EMVT is mainly composed of three modules: 1) a key factor sampling (KFS) module, which is used to mine the key factors of the image to perform low-rank reconstruction of highly structured features; 2) a non-local sparse context fusion (NSCF) module, which is used to efficiently model the global context information of shallow texture features; and 3) a non-local multi-scale context aggregation (NMCA) module, which can be applied to cross-level features to collect long-range dependencies from multiple scales. Undeniably, the newly proposed decomposable positional encoding plays a vital role in the performance improvement of the above modules. Extensive experiments are conducted on the CT scans of 130 CVD patients under 4-fold cross-validation and have demonstrated our CAC-EMVT notably outperforms the state-of-the-art methods in terms of both the mean Dice similarity coefficient (mDice) of 75.39%± 3.17 and mean surface distance (MSD) of 1.93%± 0.46. This reveals the effectiveness and the potential of our model in the clinical setting.