Text plays a fundamental yet understudied role as a narrative device in data visualization. While existing research has extensively explored text as data input and interaction modality, its function in supporting storytelling and interpretation remains fragmented. To address this gap, this work presents a systematic review of 98 publications that provide insights into using text as narrative. We investigate how text can be utilized in visualization, analyze its functions and effects, and explore how it can be designed to facilitate data communication. Our synthesis identifies significant research gaps in this domain and proposes future directions to advance the integration of text and visualization, ultimately aiming to provide guidance for designing text that enhances narrative clarity and fosters engagement.

Abstract Medical image segmentation is critical for accurate diagnosis, treatment planning, and surgical navigation. In recent years, large multitask segmentation models have often struggled due to the limited size of datasets and significant variability in target structures, image resolutions, and annotation standards. These variations can introduce task competitions during multitask model training, which hinder effective feature learning. To address these challenges, we propose PatchMoE, a unified framework designed to compensate for resolution discrepancies across datasets and feature conflicts arising in mixed-dataset training. PatchMoE is the first to introduce patch-based contrastive learning into medical image segmentation tasks, which divides images into equal-sized patches represented in 3D coordinate space. This novel approach ensures that mixed datasets with varying resolutions can be trained in a unified manner, preserving spatial relationships and enhancing contextual understanding. PatchMoE also incorporates a mixture of experts (MoE) mechanism into the decoder, which dynamically selects dataset-specific expert combinations. This design mitigates parameter conflicts through network sparsification, effectively resolving optimization conflicts in multitask datasets. The effectiveness of the proposed method was demonstrated in four independent segmentation tasks: retinal vessel (DRIVE), near-infrared blurred vessel (HVNIR), abdominal multiorgan (Synapse), and polyp segmentation (Kvasir-SEG). We compared performance using multiple metrics, including Dice score, Intersection over Union (IoU), and Hausdorff distance (HD). Compared with the state-of-the-art (SOTA) GCASCADE model, PatchMoE achieved an improvement of 3.04% in the mean Dice score across all tasks. The proposed method also achieved an average Dice score improvement of 0.88% compared to four independently trained SOTA models for each individual task. In summary, PatchMoE combines patch-based contrastive learning with dataset-informed expert gating to provide promising solutions for dataset conflicts in large transformer-based medical segmentation models.

The advances in AI-enabled techniques have accelerated the creation and automation of visualizations in the past decade. However, presenting visualizations in a descriptive and generative format remains a challenge. Moreover, current visualization embedding methods focus on standalone visualizations, neglecting the importance of contextual information for multi-view visualizations. To address this issue, we propose a new representation model, Chart2Vec, to learn a universal embedding of visualizations with context-aware information. Chart2Vec aims to support a wide range of downstream visualization tasks such as recommendation and storytelling. Our model considers both structural and semantic information of visualizations in declarative specifications. To enhance the context-aware capability, Chart2Vec employs multi-task learning on both supervised and unsupervised tasks concerning the cooccurrence of visualizations. We evaluate our method through an ablation study, a user study, and a quantitative comparison. The results verified the consistency of our embedding method with human cognition and showed its advantages over existing methods.

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

With the incredible growth of the scale and complexity of datasets, creating proper visualizations for users becomes more and more challenging in large datasets. Though several visualization recommendation systems have been proposed, so far, the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry. In this paper, we proposed AVA, an open-sourced web-based framework for Automated Visual Analytics. AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively. The code is available at https://github.com/antvis/AVA.

Imaging blood vessel networks is useful in many biomedical applications, such as injection-assist, cancer detection, various surgery, and vein identification. In NIR (near-infrared) transillumination imaging, we can visualize the subcutaneous blood vessel network. However, such images are severely blurred by the strong scattering of body tissue, and it remains challenging for most models to accurately segment these blurred images. In addition, the convolution operation in the deep learning approach means that it extracts a mixture of blurred edges and clear centers, resulting in gradual distortion during upsampling. In this paper, we propose a novel and efficient deep learning model called TRC-Unet for segmenting blurred NIR images. The transformer connection (TRC) block extracts global spatial information from different scales by adaptively suppressing scattering and increasing the clarity of features. Our proposed transformer feature fusion (TFF) module closes the gap between the highly semantic feature maps of CNN and the adaptive fuzzy transformer output to enable a precise reconstruction of the segmentation. We evaluated TRC-Unet on both a simulated blurred DRIVE dataset and a NIR vessel dataset, and we achieved competitive results. (i.e., 83.86% Dice score on DRIVE and an average boost of 4.6% on simulated images at different depths).

Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations.