Existing RGB-D Salient Object Detection (SOD) methods are primarily built on the end-to-end prediction paradigm. Although these methods have achieved remarkable progress, they still struggle to generate accurate predictions in some complex scenes due to their lack of error correction capability. In this paper, we explore the use of conditional diffusion architectures for RGB-D SOD, producing saliency maps in a step-by-step generation paradigm. Accordingly, we proposeDiffRGBD, a novel diffusion-driven framework with temporal modulation. The core of DiffRGBD is using time steps to control the conditional information injected into the denoising network in a two-stage temporal modulation manner. Specifically, our DiffRGBD comprises a feature extractor, a conditional generator, two temporal modulators, and a denoising network. First, the SAM2 encoder with adapters is adopted to extract hierarchical cross-modal features. Then, the Mutual-Differential Attention Module is responsible for generating the conditional information via effective cross-modal fusion. Notably, the conditional information continuously achieves channel modulation and spatial modulation in the Temporal Channel Enhancement Module and the Temporal Spatial Refinement Module (i.e., two temporal modulators), resulting in comprehensive conditional information. Finally, conditional information is injected into the denoising network to guide the production of saliency maps. As the time step increases, our DiffRGBD can gradually correct errors and generate accurate saliency maps. Extensive experiments on seven public RGB-D SOD benchmarks demonstrate that our proposed DiffRGBD achieves superior performance over state-of-the-art methods. The code and results of our method are available at https://github.com/Shixiang02/DiffRGBD.

With the advancement of high-throughput sequencing technology, large amounts of multi-omics data have been accumulated. Due to the comprehensive representation of different molecular layers, multi-omics data enable a deep understanding of cancer analysis, including subtype classification, biomarker identification and so on. However, existing multi-omics integration methods either rely on complex feature engineering or fail to capture the heterogeneity of multi-source data. To integrate multi-omics data in a complementary and collaborative manner, we propose a Kolmogorov-Arnold Networks-based multi-omics integration analysis framework, termed MOKAN. In detail, MOKAN first employs a sample-weighted random sampler to reduce the difference in the number of samples involved in training. Then, to perform this data analysis, our proposed model, MOKAN, leverages KAN's architecture, utilizing its learnable edge-weight activation functions and flexible network structure. This design makes MOKAN particularly well-suited for handling multi-omics data by efficiently capturing diverse feature spaces across different omics layers. It integrates heterogeneous data types while preserving the unique contributions of each omics for comprehensive and accurate representation. Moreover, MOKAN employs KAN's decomposition property to break down high-dimensional complex data into multiple low-dimensional subspaces, each independently capturing a specific aspect of the data. These subspace representations are then systematically integrated through a one-dimensional function that overlays and combines their contributions, effectively reconstructing the global data structure. Experimental results demonstrate that our proposed model outperforms other existing methods in cancer classification tasks.

Remote sensing image semantic segmentation has widespread applications in urban planning and land monitoring. In recent years, U-Net and its variant networks have almost dominated the research in the field of semantic segmentation. However, many models pay less attention to computational efficiency, rendering them ineffective in scenarios with computational resource and timeliness constraints, such as autonomous driving and disaster monitoring. To address this issue, we propose the USA-Net (UNet-like with Shifted Axial), a lightweight hybrid model based on convolution and MLP (Multi-Layer Perceptron). Specifically, we design the ST Block (Shift Tokenized Block), which introduces local features into global operations in MLP through spatial shift, and then use ELCM (Efficient Large-kernel Convolution Module) to enlarge the model’s receptive field and learn the shape features of objects. Additionally, we propose a new semi-supervised learning framework to further improve the model’s generalization performance. On the ISPRS Vaihingen and ISPRS Potsdam datasets, USA-Net significantly outperforms most state-of-the-art methods in terms of segmentation accuracy and efficiency.

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top- k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.

Multi-omics data provides a wealth of information concerning disease mechanisms, which benefits the exploration of the intricate molecular phenomena underlying diseases. In recent years, considerable endeavors have been directed towards the combination of graph convolutional network, which has the powerful ability to gather information, with multi-omics learning methods to obtain more reliable results. For achieving this pursuit, an essential challenge is data integration. Against this backdrop, we propose a unified framework named multi-level knowledge integration with graph convolutional network, which effectively incorporates multiple prior knowledge and omics data to learn an intrinsic representation. In specific, the model consists of two subnetworks: an attribute-level module and a sample-level module. The former firstly aggregates the knowledge given by the prior biological graphs into low-dimensional embeddings, and then maximizes the consistency between these prior views via optimizing a contrastive loss for attaining the attribute-based representations. The latter leverages an encoder to dimensionalize the original multi-omics data to attain more dominant sample knowledge, and subsequently utilizes another contrastive loss to align these representations between multiple omics for learning the global sample-level information. Comprehensive experiments are performed to show that the proposed model surpasses other state-of-the-art methods.

Unsupervised clustering is a crucial issue in data mining and pattern recognition. Based on deep learning paradigms, deep clustering algorithms have been studied extensively and obtained superior performance in various applications. However, most of previous methods did not use helpful information from neighborhood relations to form group-separated space, and the feature embedding is usually distorted during the training process. To tackle the former limitation, we develop a graph convolution based unsupervised learning algorithm named Stacked Graph Autoencoder (SGAE). Specifically, SGAE utilizes the message passing mechanism to aggregate information from neighbors and obtain a meaningful and separated latent representation. Since the adjacency matrix is unavailable in clustering tasks, a graph construction approach with two pruning strategies is introduced to generate a transition matrix. To reduce the distortion caused by the multi-layered network training process, we further propose a topological structure preservation mechanism. It uses the constructed adjacency graph as supervised information, to maintain the relationship between nodes in the original space. Experiments on several popular benchmark datasets show that SGAE achieves significant improvements compared to unsupervised and semi-supervised deep clustering methods.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning. However, existing methods have not fully exploited these graphical priors since none have been able to integrate knowledge from multiple sources simultaneously. To solve this problem, we propose a multi-omics data analysis framework by incorporating multiple prior knowledge into graph neural network (MPK-GNN). To the best of our knowledge, this is the first attempt to introduce multiple prior graphs into multi-omics data analysis. Specifically, the proposed method contains four parts: (1) a feature-level learning module to aggregate information from prior graphs; (2) a projection module to maximize the agreement among prior networks by optimizing a contrastive loss; (3) a sample-level module to learn a global representation from input multi-omics features; (4) a task-specific module to flexibly extend MPK-GNN for various downstream multi-omics analysis tasks. Finally, we verify the effectiveness of the proposed multi-omics learning algorithm on the cancer molecular subtype classification task. Experimental results show that MPK-GNN outperforms other state-of-the-art algorithms, including multi-view learning methods and multi-omics integrative approaches.

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or modalities. With the rapidly growing attention of graph neural networks, more and more researchers have introduced various graph models into multi-view learning. Although considerable achievements have been made, most existing methods usually propagate information in a single view and fuse multiple information only from the perspective of attributes or relationships. To solve the aforementioned problems, we propose an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and apply it to deep multi-view clustering tasks. The proposed method is designed with three submodules and has the following merits: a) The proposed view-specific and cross-view propagation modules can capture the consistency and complementarity information among multiple views; b) The designed fusion module performs multi-view information fusion with the attributes of nodes and the relationships among them simultaneously. Experiments on popular databases show that DFP-GNN achieves significant results compared with several state-of-the-art algorithms.