Pathology images are crucial for cancer diagnosis and treatment. Although artificial intelligence has driven rapid advancements in pathology image analysis, the interpretation of ultra-large and multi-scale pathology images in clinical practice still heavily relies on physicians' experience. Clinicians need to repeatedly zoom in and out on individual slides to compare and assess pathological details - a process that is both time-consuming and prone to visual fatigue. The system first employs a diffusion model to perform tissue segmentation on pathology images, then calculates pathological tissue proportions and morphological metrics. Finally, through multi-scale dynamic comparison and multi-level visual evaluation, the system facilitates comprehensive and precise analysis of pathology images. The system provides clinicians with an intelligent and interactive tool for pathology image interpretation, enabling efficient visualization and precise analysis of pathological details, thereby reducing the effort require for detailed analysis.

Pathology image segmentation plays a pivotal role in artificial digital pathology diagnosis and treatment. Existing approaches to pathology image segmentation are hindered by labor-intensive annotation processes and limited accuracy in tail-class identification, primarily due to the long-tail distribution inherent in gigapixel pathology images. In this work, we introduce the Laplace Diffusion Model, referred to as L-Diffusion, an innovative framework tailored for efficient pathology image segmentation. L-Diffusion utilizes multiple Laplace distributions, as opposed to Gaussian distributions, to model distinct components—a methodology supported by theoretical analysis that significantly enhances the decomposition of features within the feature space. A sequence of feature maps is initially generated through a series of diffusion steps. Following this, contrastive learning is employed to refine the pixel-wise vectors derived from the feature map sequence. By utilizing these highly discriminative pixel-wise vectors, the segmentation module achieves a harmonious balance of precision and robustness with remarkable efficiency. Extensive experimental evaluations demonstrate that L-Diffusion attains improvements of up to 7.16%, 26.74%, 16.52%, and 3.55% on tissue segmentation datasets, and 20.09%, 10.67%, 14.42%, and 10.41% on cell segmentation datasets, as quantified by DICE, MPA, mIoU, and FwIoU metrics. The source are available at https://github.com/Lweihan/LDiffusion.

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinson's Progression Markers Initiative.

With the rapid development of mobile Internet, the popularity of video capture devices has brought a surge in multimedia video resources. Utilizing machine learning methods combined with well-designed features, we could automatically obtain video summarization to relax video resource consumption and retrieval issues. However, there always exists a gap between the summarization obtained by the model and the ones annotated by users. How to help users understand the difference, provide insights in improving the model, and enhance the trust in the model remains challenging in the current study. To address these challenges, we propose VSumVis under a user-centered design methodology, a visual analysis system with multi-feature examination and multi-level exploration, which could help users explore and analyze video content, as well as the intrinsic relationship that existed in our video summarization model. The system contains multiple coordinated views, i.e., video view, projection view, detail view, and sequential frames view. A multi-level analysis process to integrate video events and frames are presented with clusters and nodes visualization in our system. Temporal patterns concerning the difference between the manual annotation score and the saliency score produced by our model are further investigated and distinguished with sequential frames view. Moreover, we propose a set of rich user interactions that enable an in-depth, multi-faceted analysis of the features in our video summarization model. We conduct case studies and interviews with domain experts to provide anecdotal evidence about the effectiveness of our approach. Quantitative feedback from a user study confirms the usefulness of our visual system for exploring the video summarization model.

Volume rendering techniques have been developed for decades and have been widely applied in many research fields, such as medical image visualization, geological exploration, scientific computing. etc. With the maturity of volume visualization techniques, one may have many choices to analyze volume data. However, facing different application requirements in specific cases, one may need pertinent methods to visualize volume data and highlight specific volume features. In this paper, we review and classify the existing literature on feature enhancement volume rendering. The classification is conducted based on the enhancement of four types of features (the external feature, the internal feature, the structure feature, and the ideographic feature) in volume data. Finally, we conclude this survey with future challenges in feature enhancement volume visualization.