Accurate cancer prognosis is critical for improving patient outcomes. However, existing models often struggle to handle high-dimensional and heterogeneous multi-omics data. This paper proposes DADRSurv, a novel deep learning method that effectively integrates multi-omics data, including mRNA and miRNA, with clinical variables. The proposed architecture uniquely combines a dual variational autoencoder (VAE) with a dual recurrent neural network (RNN). Specifically, the model utilizes cascaded encoders for robust feature extraction from high-dimensional omics data and employs an RNN with a timeaware attention mechanism to dynamically capture complex temporal relationships for survival prediction. Extensive experiments on TCGA breast (BRCA) and ovarian (OV) cancer datasets demonstrate that DADRSurv outperforms various state-of-theart models in prognostic prediction, achieving concordance index (C-index) scores of 0.763 (BRCA) and 0.662 (OV) on multi-modal data. Furthermore, the model also effectively stratiffes patients into distinct high- and low-risk groups (Log-rank$p<0.0001$). The experimental results prove its power as a robust framework for cancer survival analysis and shows its potential to enhance clinical decision-making.

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose accurate segmentation is a prerequisite for patient treatment and prognosis. Location and area of lymph nodes (LN) provide significant information for patient staging. However, existing methods for segmenting primary NPC tumors and LNs ignore three major challenges, i.e., uncertain position, irregular boundary, and false negative, thus, obtaining unsatisfactory segmentation results. Given the remarkable success of capturing global context information in Transformer, we have tailored a Transformer-based architecture, named NPCTrans, to address these limitations. NPCTrans is underpinned by three critical modules, including the position locating module (PLM), the boundary attention module (BAM), and the lesion region correction module (LRC). The PLM aims to improve sensitivity towards lesion regions by utilizing relative position encodings and control position information by gated in global attention. The BAM captures and refines the key points of the irregular boundary to enhance boundary segmentation performance. The LRC focuses on multi-scale feature learning by augmenting features of pixels and regions of interest at different scales to correctly identify multiple lesion regions to reduce false negatives. Extensive benchmarking experiments are conducted on our benchmarking dataset, which includes 9,100 samples collected from 723 patients, and results demonstrate that NPCTrans outperforms existing state-of-the-art models.

Identifying cancer subtypes holds essential promise for improving prognosis and personalized treatment. Cancer subtyping based on multi-omics data has become a hotspot in bioinformatics research. One of the critical approaches of handling data heterogeneity in multi-omics data is first modeling each omics data as a separate similarity graph. Then, the information of multiple graphs is integrated into a unified graph. However, a significant challenge is how to measure the similarity of nodes in each graph and preserve cluster information of each graph. To that end, we exploit a new high order proximity in each graph and propose a similarity fusion method to fuse the high order proximity of multiple graphs while preserving cluster information of multiple graphs. Compared with the current techniques employing the first order proximity, exploiting high order proximity contributes to attaining accurate similarity. The proposed similarity fusion method makes full use of the complementary information from multi-omics data. Experiments in six benchmark multi-omics datasets and two individual cancer case studies confirm that our proposed method achieves statistically significant and biologically meaningful cancer subtypes.

DNA sharing and querying of personal genomic sequences have becoming more critical than ever with the accumulation of large-scale biomedical data. Quantitative genomic studies heavily rely on multi-source DNA datasets from different institutions, who are reluctant to share the data via cloud centers. The high sensitivity of DNA has compelled the government to restrict its acquisition and usage. One potential solution to tackle the issue is designing a secure query strategy on the encrypted DNA datasets. However, the popular secure DNA query schemes remain defective in verifiability, reliability, and NDA privacy. To relieve the issues, we propose a DNA searchable encryption method named EncGD to achieve verifiable privacy-preserving queries and reliable updates on multi-source dynamic DNA datasets. Specifically, the proposed scheme EncGD is designed by the plaintext-related permutation and substitution primitives, which can enhance the DNA privacy due to the chosen-plaintext attack (CPA)-resist ability. Furthermore, the method realizes the verifiability and reliability by adding known information to block the malicious behaviors of clouds. Experimental results demonstrate the superior efficiency of the proposed method comparing with the two state-of-the-art schemes in terms of time and space costs.

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose accurate segmentation is a prerequisite for treatment. However, existing deep learning methods achieve unsatisfactory segmentation performance on NPC MR images, since NPC is infiltrative with small ambiguous boundary volume, making it indiscernible from tightly connected surrounding and complex backgrounds. To address the issues, a NPC segmentation network, termed NPCFormer, is proposed. The NPCFormer consists of two modules, Skip Residual Transformer (SRT) and Boundary Attention Unit (BAU), which are designed for NPC segmentation. The two modules are proposed via redesigning the multi-head self-attention to achieve accurate segmentation of NPC. The SRT exploits the global position context for the NPC locating. The BAU discriminates the tumor boundaries from its surrounding tissues by utilizing the global position context. Extensive experiments on our dataset demonstrate the proposed NPCFormer could distinguish and segment NPC from complex background tissues accurately.

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance image (MRI) is important for treatment planning and follow-up evaluation. However, it is still challenging because NPC is infiltrative with a vague border and has a tiny volume with varying sizes and shapes. The above problems easily cause the NPC tumors’ features to submerge in the process of feature extraction. Standard segmentation methods do not cope with the “feature submergence” and performed unsatisfactorily in NPC segmentation. To address the problem, a dual-supervised method equipped with the detection-and-excitation module (DEM) was proposed. DEM strengthens the region of interest (ROI) and weakens complex and immense background through incorporating detection feature maps (outline maps) with intermediated segmentation features. To be specific, the DEM explored the outline of the ROI and output a detection feature map by using a supervised layer guided by a tailored loss function. Then, the DEM uses the results to recalibrates the intermediated segmentation. Finally, a neural network equipped with the proposed DEM was elaborately designed to achieve accurate segmentation. We applied the proposed network termed as DENet on a real nasopharyngeal carcinoma dataset to realize automatic NPC tumors segmentation in multi-modality MR images. The proposed DEM enables the neural network to segment NPC tumors within the immense and complex background. Experimental results have demonstrated the effectiveness of the proposed method by comparing it with benchmark models.

Multi-view clustering aims to partition objects into potential categories by utilizing cross-view information. One of the core issues is to sufficiently leverage different views to learn a latent subspace, within which the clustering task is performed. Recently, it has been shown that representing the multi-view data by a tensor and then learning a latent self-expressive tensor is effective. However, early works mainly focus on learning essential tensor representation from multi-view data and the resulted affinity matrix is considered as a byproduct or is computed by a simple average in Euclidean space, thereby destroying the intrinsic clustering structure. To that end, here we proposed a novel multi-view clustering method to directly learn a well-structured affinity matrix driven by the clustering task on Grassmann manifold. Specifically, we firstly employed a tensor learning model to unify multiple feature spaces into a latent low-rank tensor space. Then each individual view was merged on Grassmann manifold to obtain both an integrative subspace and a consensus affinity matrix, driven by clustering task. The two parts are modeled by a unified objective function and optimized jointly to mine a decomposable affinity matrix. Extensive experiments on eight real-world datasets show that our method achieves superior performances over other popular methods.

The human retinal surface resembles to a sphere while it is captured by two-dimensional (2D) planar imaging to have a stereo sequence in clinical practice. Reconstructing its three-dimensional (3D) structure from the 2D planar retinal images is crucial for analyzing the relationship between the topological morphology and clinical implication. In this regard, we propose to reconstruct the 3D retina structure from 2D stereo fundus images via dynamic registration. The fundus images from different viewpoints are first co-registrated by using multi-scale deep convolutional feature and geometric structure feature by building their transformation function. The aligned images are then mosaicked together and a 3D reconstruction is obtained by a learned weighted smoothing project the registered images onto 3D coordinates. We compare the proposed registration method with five state-of-the-art methods. Extensive experimental results demonstrate that the proposed framework achieves superior performances, even with challenging scenarios in which the tested images are severely degraded by illness, large eyeball rotation and low resolutions.

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC. The online predictive system is freely available to the public (http://212.64.70.65:8000/).

Accurate nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance image (MRI) is crucial for diagnosis and treatment. However, most existing deep learning methods performed unsatisfactorily, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable boundary, making it indiscernible from tightly connected surrounding tissue in immense and complex background. To address the background dominant problem, this paper proposes a coarse-to-fine deep model. The proposed model starts with predicting a coarse mask with a well-designed segmentation module, followed by a boundary rendering module, which exploits semantic information from different layers of feature maps to refine the boundary of the coarse mask. The designed rendering module is shown to achieve superior performance with dramatically fewer parameters by operating only on the segmented mask, rather than on the whole feature maps as the popular methods do. Extensive experiments are conducted on a collected dataset consisting of 2000 MRI slices from 596 patients. Experimental results demonstrate that the proposed model not only outperforms six popular segmentation models but also has a considerable generalization capability on existing models.