Segmenting the brain magnetic resonance (MR) images to region-of-interest (ROI) is a fundamental step for many medical image analysis tasks. Convolutional neural networks (CNNs) excel in learning the high-level contextual features for image segmentation. However, such high-level features are low-order features, which cannot reflect the complex appearance patterns of brain MR images. Intuitively, using the high-order features can enhance the performance of CNNs. Therefore, in this paper, we propose a novel Efficient Covariance Network (EfficientCovNet) that models pairwise voxel dependency features and applies it to the brain ROI segmentation tasks. Our EfficientCovNet consists of two pathways: a pairwise voxel dependency feature learning pathway that uses a novel covariance convolution to efficiently capture the pairwise features from MR images, and a contextual feature learning pathway that extracts high-level contextual features using convolutional operations. The pairwise features and contextual features are then fused together to boost brain ROI segmentation performance. Experimental results on five datasets, i.e., IXI, LONI-LPBA40, OASIS, ADNI, and CC359 datasets, demonstrate that our EfficientCovNet achieves superior performance for brain ROI segmentation in comparison with the state-of-the-art methods.

Real-time tissue tracking is a fundamental task in liver ultrasound applications. Due to the periodic nature of liver motion, historical trajectories can offer valuable priors for target localization, particularly when foreground-background distinction is weak. However, existing trackers often exploit these trajectories as shortcuts, relying excessively on periodic respiratory patterns rather than true object appearance matching. In this work, we revisit liver tracking from a causal perspective and propose CUSTrack, a method that mitigates periodicity bias by decomposing and correcting the total causal effect of historical trajectories. We define periodicity bias as the direct causal effect of past states and eliminate it via counterfactual reasoning, preserving 'good' trajectory priors while suppressing 'bad' periodic bias. To ensure identifiability, we incorporate a deconfounding module that removes latent confounders from fused feature representations. Extensive experiments on liver ultrasound datasets demonstrate that CUSTrack achieves superior tracking accuracy and robustness under challenging conditions.

MOTIVATION: Oxford Nanopore Technologies (ONT) direct RNA sequencing (dRNA-seq) offers high-resolution, single-molecule analysis but is hindered by the lack of robust multiplex barcoding methods. Existing approaches struggle to accurately demultiplex raw nanopore signals, failing to capture both local patterns and long-range dependencies. This limitation underscores the requirement for advanced solutions to improve accuracy, efficiency, and adaptability in sequencing workflows. We present DemuxTrans, a hybrid deep learning framework that integrates Multi-Layer Feature Fusion, Transformers, and Temporal Convolutional Networks (TCN) for precise barcode demultiplexing. RESULTS: DemuxTrans achieves state-of-the-art performance across multiple datasets by effectively balancing local feature extraction, global context modeling, and long-term dependency capture, excelling in metrics such as accuracy, recall and F1-score. These results demonstrate DemuxTrans as a scalable, efficient solution for barcode demultiplexing in nanopore sequencing, enabling precise identification of multiplexed RNA samples and improving throughput in transcriptomic and epigenomic analyses. AVAILABILITY AND IMPLEMENTATION: The code and datasets are publicly available on https://github.com/LiyuanShu116/Demuxtrans.

Accurate spine segmentation is crucial in clinical diagnosis and treatment of spine diseases. However, due to the complexity of spine anatomical structure, it has remained a challenging task to accurately segment spine images. Recently, the segment anything model (SAM) has achieved superior performance for image segmentation. However, generating high-quality points and boxes is still laborious for high-dimensional medical images. Meanwhile, an accurate mask is difficult to obtain. To address these issues, in this paper, we propose a multi-atlas guided SAM using multiple pseudo mask prompts for spine image segmentation, called MA-SAM. Specifically, we first design a multi-atlas prompt generation sub-network to obtain the anatomical structure prompts. More specifically, we use a network to obtain coarse mask of the input image. Then atlas label maps are registered to the coarse mask. Subsequently, a SAM-based segmentation sub-network is used to segment images. Specifically, we first utilize adapters to fine-tune the image encoder. Meanwhile, we use a prompt encoder to learn the anatomical structure prior knowledge from the multi-atlas prompts. Finally, a mask decoder is used to fuse the image and prompt features to obtain the segmentation results. Moreover, to boost the segmentation performance, different scale features from the prompt encoder are concatenated to the Upsample Block in the mask decoder. We validate our MA-SAM on the two spine segmentation tasks, including spine anatomical structure segmentation with CT images and lumbosacral plexus segmentation with MR images. Experiment results suggest that our method achieves better segmentation performance than SAM with points, boxes, and mask prompts.

Segment Anything Model (SAM) is a foundational image segmentation model, which shows superior performance for natural image segmentation tasks. Several SAM-based medical image segmentations have been proposed. However, these SAM-based medical image segmentation methods heavily depend on prior manual guidance involving points, boxes, and coarse-grained masks, which lack adaptability and flexibility. Moreover, the inherent challenge of edge blurring in medical images is critical, as it directly affects the quality of segmentation. To address these challenges, we propose an uncertainty-driven edge prompt generation network for medical image segmentation, called UDEG-Net. Specifically, to better adapt to medical image segmentation, we fine-tune the encoder by using Low-Rank Adaptation (LoRA) technology to enhance the encoder's learning capability and capture enriched medical image features. Furthermore, to overcome the limitations of interactive prompts, we develop an auto edge prompt generator to generate edge prompt information and further enhance the structural representation. Finally, to focus on the high-uncertainty edge areas, we introduce an evidence-based uncertainty estimation and a progressive uncertainty-driven loss to drive the auto edge prompt generator to yield robust edge prompt information and reliable segmentation results. Experimental results on three public datasets and one private dataset show that our UDEG-Net outperforms the state-of-the-art medical image segmentation methods.

Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects poses major challenges in scRNA-seq analysis due to data distribution variation across batches. Although several batch effect mitigation algorithms have been proposed, most of them focus only on the correlation of local structure embeddings, ignoring global distribution matching and discriminative feature representation in batch correction. In this paper, we proposed the discriminative domain adaption network (D2AN) for joint batch effects correction and type annotation with single-cell RNA-seq. Specifically, we first captured the global low-dimensional embeddings of samples from the source and target domains by adversarial domain adaption strategy. Second, a contrastive loss is developed to preliminarily align the source domain samples. Moreover, the semantic alignment of class centroids in the source and target domains is achieved for further local alignment. Finally, a self-paced learning mechanism based on inter-domain loss is adopted to gradually select samples with high similarity to the target domain for training, which is used to improve the robustness of the model. Experimental results demonstrated that the proposed method on multiple real datasets outperforms several state-of-the-art methods.

Brain region-of-interest (ROI) segmentation with magnetic resonance (MR) images is a basic prerequisite step for brain analysis. The main problem with using deep learning for brain ROI segmentation is the lack of sufficient annotated data. To address this issue, in this paper, we propose a simple multi-atlas supervised contrastive learning framework (MAS-CL) for brain ROI segmentation with MR images in an end-to-end manner. Specifically, our MAS-CL framework mainly consists of two steps, including 1) a multi-atlas supervised contrastive learning method to learn the latent representation using a limited amount of voxel-level labeling brain MR images, and 2) brain ROI segmentation based on the pre-trained backbone using our MSA-CL method. Specifically, different from traditional contrastive learning, in our proposed method, we use multi-atlas supervised information to pre-train the backbone for learning the latent representation of input MR image, i.e., the correlation of each sample pair is defined by using the label maps of input MR image and atlas images. Then, we extend the pre-trained backbone to segment brain ROI with MR images. We perform our proposed MAS-CL framework with five segmentation methods on LONI-LPBA40, IXI, OASIS, ADNI, and CC359 datasets for brain ROI segmentation with MR images. Various experimental results suggested that our proposed MAS-CL framework can significantly improve the segmentation performance on these five datasets.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation harm to human bodies, is now attracting increasing interest in the medical imaging field. As the image quality is degraded by low dose radiation, LDCT exams require specialized reconstruction methods or denoising algorithms. However, most of the recent effective methods overlook the inner-structure of the original projection data (sinogram) which limits their denoising ability. The inner-structure of the sinogram represents special characteristics of the data in the sinogram domain. By maintaining this structure while denoising, the noise can be obviously restrained. Therefore, we propose an LDCT denoising network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise by utilizing the inner-structure in the sinogram domain. Specifically, we study the CT imaging mechanism and statistical characteristics of sinogram to design the sinogram inner-structure loss including the global and local inner-structure for restoring high-quality CT images. Besides, we propose a sinogram transformer module to better extract sinogram features. The transformer architecture using a self-attention mechanism can exploit interrelations between projections of different view angles, which achieves an outstanding performance in sinogram denoising. Furthermore, in order to improve the performance in the image domain, we propose the image reconstruction module to complementarily denoise both in the sinogram and image domain.

Privilege separation (privsep) is an effective technique for improving software’s security, but privsep involves decomposing software into components and assigning them different privileges. This is often laborious and error-prone. This paper contributes the following for applying privsep to C software: (1) a portable, lightweight, and distributed runtime library that abstracts externally-enforced compartment isolation; (2) an abstract compartmentalization model of software for reasoning about privsep; and (3) a privsep-aware Clang-based tool for code analysis and semi-automatic software transformation to use the runtime library. The evaluation spans 19 compartmentalizations of third-party software and examines: Security: 4 CVEs in widely-used software were rendered unexploitable; Approximate Effort Saving: on average, the synthesis-to-annotation code ratio was greater than 11.9 (i.e., 10 × lines of code were generated for each annotation); and Overhead: execution-time overhead was less than 2%, and memory overhead was linear in the number of compartments.