Data economics drives AI by optimizing data usage, reducing costs, and enhancing efficiency. In 3D tumor segmentation, efficiency is crucial due to the high demand for labor-intensive manual annotations. Box-supervised segmentation offers a promising alternative but is constrained by tumor morphology complexity and boundary ambiguity. In this paper, we propose a novel 3D tumor segmentation model that integrates both positional and embedding features to facilitate inter-task collaboration. We introduce an Anatomical-Driven Class Activation Map to predefine the complex tumor morphology prior, which is further refined by our Geometric Pixel Co-embedding Learner. This learner utilizes contrastive learning to encode semantic information between center and edge pixels, enhancing pixel clustering and progressively refining tumor boundary segmentation in a coarse-to-fine manner. Our approach outperforms existing box-supervised methods in segmentation performance, with extensive experiments on four tumor datasets demonstrating significant improvements. This work provides a cost-effective and efficient solution for tumor segmentation, advancing the application of data economics in medical imaging.

Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial human effort due to the complex spatial correspondence between each pair of samples. To address this challenge, we first proposed a GNN-based model to predict cross-sample cell developmental trajectories. We then developed TrajLens, a visual analytics system that supports biologists in exploring and refining the cell developmental trajectories based on predicted links. Specifically, we designed the visualization that integrates features on cell distribution and developmental direction across multiple samples, providing an overview of the spatial evolutionary patterns of cell populations along trajectories. Additionally, we included contour maps superimposed on the original cell distribution data, enabling biologists to explore them intuitively. To demonstrate our system's performance, we conducted quantitative evaluations of our model with two case studies and expert interviews to validate its usefulness and effectiveness.

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks—an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.

Deep learning-based tumor segmentation methods typically require precise pixel-level annotations, which are costly in clinical practice. While bounding box supervision offers a more efficient alternative, existing approaches assume unrealistically tight box annotations, leading to performance degradation when applied to the loose boxes commonly produced by medical annotators. To address this challenge, we propose LooBox, a novel 3D segmentation framework that utilizes loose box annotations through a self-correction and bidirectional rectification paradigm. For the self-correction part, we propose a noise cleaner that comprehensively utilizes deterministic outer box information by integrating three complementary perspectives for predictive self-rectification: entropy mapping, gradient monitoring, and foreground-background affinity measurement. For the bidirectional rectification part, we introduce an augmentation-driven comprehensive consistency constraint strategy. Specifically, the framework incorporates: an asymmetric co-teaching architecture comprising a basic UNet and an enhanced UNet variant with a noise adapter, and an augmentation-driven consistency mechanism that computes pairwise loss between self-corrected predictions after each training iteration to ensure robust tumor feature extraction. Comprehensive evaluations on LIDC-IDRI, MSD-Lung, and MSD-Pancreas datasets demonstrate that LooBox achieves superior segmentation accuracy compared to state-of-the-art box-supervised methods.

Abstract Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time‐intensive. To address these challenges, we propose IntelliCircos, an AI‐powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain‐specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.

Palmprint recognition has emerged as a prominent biometric authentication method due to its high discriminative power, making it suitable for IoT-based security applications. However, the traditional verification paradigm—requiring identical query and registered palmprints—poses notable limitations. This approach is inconvenient if the registered palmprint is injured. To address these challenges, we draw inspiration from biological insights into the symmetrical development of structures during embryonic growth and propose a novel Cross-Chirality Palmprint Verification (CCPV) framework. CCPV enables authentication using either palm, irrespective of which palm is registered, enhancing flexibility for IoT deployments with diverse user conditions. CCPV incorporates an innovative matching rule to improve robustness and minimize variability. This rule calculates distances by flipping the gallery and query palmprints, averaging the results to produce the final matching score. Considering all potential alignments, this approach reduces variance and boosts reliability, which is critical for ensuring seamless biometric authentication in IoT systems. Complementing this is the cross-chirality (CC) loss, which fosters a robust feature space tailored to cross-chirality matching. The CC loss ensures consistency across four palmprint variants—left, right, flipped left, and flipped right—enabling the model to extract chirality-consistent features. Extensive experiments on public datasets validate our effectiveness under closed-set and open-set scenarios. Furthermore, we demonstrate that CCPV is versatile and can seamlessly integrate with existing palmprint recognition methods to achieve superior performance. This innovation advances state-of-the-art biometric authentication and paves the way for more resilient palmprint recognition systems for IoT applications.

RNA binding proteins (RBPs) play a crucial role in regulating biological functions through their interactions with specific RNAs, significantly impacting various life processes. High-throughput experiments provide substantial data, facilitating the development of computational predictions. However, current methods struggle to effectively integrate multi-level semantic information and require enhanced predictive accuracy on small-sample datasets. To address these limitations, we propose MTP-RBP, a method that integrates multi-task pre-training with a robust pre-trained encoder. This method not only extracts deep contextual information from RNA sequences but also incorporates structural and functional knowledge for a more comprehensive semantic representation. By enhancing masked language modeling with secondary structure construction and binding function prediction pre-training tasks, MTP-RBP enables better fusion of multi-level features. Experimental results show that MTP-RBP achieves state-of-the-art performance, surpassing baseline and existing RNA language models, particularly on small datasets.

Designing synthetic routes for novel molecules is pivotal in various fields like medicine and chemistry. In this process, researchers need to explore a set of synthetic reactions to transform starting molecules into intermediates step by step until the target novel molecule is obtained. However, designing synthetic routes presents challenges for researchers. First, researchers need to make decisions among numerous possible synthetic reactions at each step, considering various criteria (e.g., yield, experimental duration, and the count of experimental steps) to construct the synthetic route. Second, they must consider the potential impact of one choice at each step on the overall synthetic route. To address these challenges, we proposed SynthLens, a visual analytics system to facilitate the iterative construction of synthetic routes by exploring multiple possibilities for synthetic reactions at each step of construction. Specifically, we have introduced a tree-form visualization in SynthLensto compare and evaluate all the explored routes at various exploration steps, considering both the exploration step and multiple criteria. Our system empowers researchers to consider their construction process comprehensively, guiding them toward promising exploration directions to complete the synthetic route. We validated the usability and effectiveness of SynthLensthrough a quantitative evaluation and expert interviews, highlighting its role in facilitating the design process of synthetic routes. Finally, we discussed the insights of SynthLensto inspire other multi-criteria decision-making scenarios with visual analytics.

Weather forecast ensembles are commonly used to assess the uncertainty and confidence of weather predictions. Conventional methods in meteorology often employ ensemble mean and standard deviation plots, as well as spaghetti plots, to visualize ensemble data. However, these methods suffer from significant information loss and visual clutter. In this paper, we propose a new approach for uncertainty visualization of weather forecast ensembles, including isovalue selection based on information loss and hierarchical visualization that integrates visual abstraction and detail preservation. Our approach uses non-uniform downsampling to select key-isovalues and provides an interactive visualization method based on hierarchical clustering. Firstly, we sample key-isovalues by contour probability similarity and determine the optimal sampling number using an information loss curve. Then, the corresponding isocontours are presented to guide users in selecting key-isovalues. Once the isovalue is chosen, we perform agglomerative hierarchical clustering on the isocontours based on signed distance fields and generate visual abstractions for each isocontour cluster to avoid visual clutter. We link a bubble tree to the visual abstractions to explore the details of isocontour clusters at different levels. We demonstrate the utility of our approach through two case studies with meteorological experts on real-world data. We further validate its effectiveness by quantitatively assessing information loss and visual clutter. Additionally, we confirm its usability through expert evaluation.

Accurate automatic segmentation of medical images has long faced challenges such as significant lesion scale variations and blurred boundaries. This study proposes a Boundary-Adaptive Transformer (BAformer) specifically designed for 2D medical image segmentation. BAformer employs a hierarchical architecture that focuses on boundary-related features across varying resolutions. Additionally, we introduce a Dense Feed-forward Network (DenseFFN) to reuse features, enabling each layer to accumulate information from previous layers. Finally, we extend the benefits of the dense network to the decoder, balancing parameter efficiency and performance. Extensive experiments demonstrate that BAformer achieves state-of-the-art performance on several challenging medical segmentation tasks. Furthermore, BAformer can seamlessly integrate with existing segmentation networks, demonstrating its versatility and effectiveness.

The identification of miRNA-disease associations (MDAs) holds significant value in the field of disease diagnosis and treatment. Recently, computational prediction methods have been increasingly proposed to detect potential MDAs, so as to assist experimental verifications. Despite the success of deep learning models, studies in the MDA prediction are still limited by the datasets and the evaluation framework employed. Concretely, existing datasets comprise only hundreds of diseases, and the random-split-based evaluation framework provides an overly optimistic estimate of the performance of MDA prediction methods. In this study, we propose a novel benchmark, Timely-MDA, for generalizable MDA prediction. First, we construct a comprehensive dataset comprising a broad scope of miRNA and disease entities and diverse semantic features. Second, four existing MDA prediction methods are implemented, and a new baseline is proposed based on our dataset. Third, the performance of these methods is analyzed using our timely-split evaluation framework. Overall, Timely-MDA provides a robust data foundation for MDA modeling, and enables quantitative estimation of the generalization ability of MDA prediction methods. Data and code are available at https://github.com/EchoChou990919/Timely-MDA.

Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refinement. Inspired by the common and widely recommended slide creation practice: drafting outlines first and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its effectiveness and usability.

Palmprint biometrics garner heightened attention in palm-scanning payment and social security due to their distinctive attributes. However, prevailing methodologies singularly prioritize texture orientation, neglecting the significant texture scale dimension. We design an innovative network for concurrently extracting intra-scale and inter-scale features to redress this limitation. This paper proposes a scale-aware competitive network (SAC-Net), which includes the Inner-Scale Competition Module (ISCM) and the Across-Scale Competition Module (ASCM) to capture texture characteristics related to orientation and scale. ISCM efficiently integrates learnable Gabor filters and a self-attention mechanism to extract rich orientation data and discern textures with long-range discriminative properties. Subsequently, ASCM leverages a competitive strategy across various scales to effectively encapsulate the competitive texture scale elements. By synergizing ISCM and ASCM, our method adeptly characterizes palm-print features. Rigorous experimentation across three benchmark datasets unequivocally demonstrates our proposed approach’s exceptional recognition performance and resilience relative to state-of-the-art alternatives.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.

Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.

With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with a disk metaphor to overview NFT transactions and a flow-based visualization module to reveal detailed NFT flows at multiple levels. We conduct two case studies and an in-depth user interview with 14 NFT investors to evaluate NFTDisk. The results demonstrate its effectiveness in exploring wash trading activities in NFT markets.

Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many computational methods have been developed to predict RNA-protein binding sites, their prediction accuracy on small sample datasets needs improvement. To overcome this limitation, we propose a novel model called SA-Net, which utilizes k-mer embedding to encode RNA sequences and a self-attention-based neural network to extract sequence features. K-mer embedding assists the model to discover significant subsequence fragments associated with binding sites. The self-attention mechanism captures contextual information from the entire input sequence globally, performing well in small sample sequence learning. Experimental results demonstrate that SA-Net attains state-of-the-art results on the RBP-24 dataset. We find that 4-mer embedding aids the model to achieve optimal performance. We also show that the self-attention network outperforms the commonly used CNN and CNN-BLSTM models in sequence feature extraction.

In this paper, we are interested in exploring the problem of full-resolution image segmentation, with the focus placed on learning full-resolution representations for biomedicine images. We divide the original resolution image into patches of different sizes in different stages and then extracte local features from large to small patches using efficient and flexible components in modern convolutional neural networks (CNN). Meanwhile, a multilayer perceptron (MLP) block intended for modeling long-range dependencies between patches is designed to compensate for the inherent inductive bias caused by convolution operations. In addition, we perform multi-scale fusion and receive representation information from parallel paths at each stage, resulting in a rich full-resolution representation. We evaluate the proposed method on different biomedical image segmentation tasks and it achieves a competitive performance compared to the latest deep learning segmentation methods. It is hoped that this method will serve as a useful alternative to biomedical image segmentation and provide an improved idea for the research based on full-resolution representation.

The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods do not sufficiently extract key features during data processing, and the learning model parts are either less powerful or overly complex. Therefore, there is still potential to achieve better predictive performance by improving these two aspects. In this work, we propose a novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder. We construct the heterogeneous network by integrating the associations between lncRNAs, diseases and micro RNAs (miRNAs). Intra-class similarities and inter-class associations are presented as the lncRNA-disease-miRNA weighted adjacency matrix to unify semantics. Next, we design a topological feature extraction process to further obtain multi-hop topological pathway features latent in the adjacency matrix. Finally, to capture the interdependencies between heterogeneous pathways, a Transformer encoder based on the global self-attention mechanism is employed to predict lncRNA-disease associations. The efficient feature extraction and the intuitive and powerful learning model lead to ideal performance. The results of computational experiments on two datasets show that our method outperforms the state-of-the-art baseline methods. Additionally, case studies further indicate its capability to discover new associations accurately.

Abundant tourism user-generated content (UGC) contains a wealth of cognitive and emotional information, providing valuable data for building destination images that depict tourists’ experiences and appraisal of the destinations during the tours. In particular, multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations. However, existing methods usually focus on the image of a single destination, and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge. Therefore, we discuss requirements with tourism experts and present MDIVis, a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison. Specifically, we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images. Finally, we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data, and the usability and effectiveness are confirmed through expert interviews.

MicroRNAs (miRNAs) are small non-coding RNAs that achieve post-transcriptional regulation of RNA silencing and gene expression by targeting messenger RNAs (mRNAs). Rapid and effective detection of miRNAs target sites is a significantly important topic in bioinformatics. In this study, a deep learning approach based on fusion of stacked denoising autoencoders (SDA) and Convolutional denoising autoencoders (CAE) is developed for sequence and structure data respectively with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed fusion model performs better in terms of the accuracy, precision, recall, AUC (Area under the curve) and Fl-score. A web system is also developed to identify and display the microRNA target sites effectively and Rapidly.

The study of tourism destination images is of great significance in the tourism discipline. Tourism user-generated content (UGC), i.e., the feedback on tourism websites, provides rich information for constructing a destination image. However, it is difficult for tourism researchers to obtain a relatively complete and intuitive destination image due to the unintuitive destination image display, the significant variance in departure time and data length, and the destination type in UGC. We propose TDIVis, a carefully designed visual analytics system, aimed at obtaining a relatively comprehensive destination image. Specifically, a keyword-based sentiment visualization method is proposed to associate the cognitive image with the emotional image, and by this method, both time evolution analysis and classification analysis are considered; a multi-attribute association double sequence visualization method is proposed to associate two different types of text sequences and provide a dynamic visual encoding interaction method for the multi-attribute characteristics of sequences. The effectiveness and usability of TDIVis are demonstrated through four cases and a user study.

Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graph layout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteria simultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attention to these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visual ambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguity in edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in community structures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others are then displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualization approaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a user to explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectiveness of the technique is demonstrated through case studies and expert reviews.