Visual Computing for Industry Biomedicine and Art最新文献

{"title":"Aligned and realistic latent diffusion for text-to-motion generation.","authors":"Zhaowu Li, Rui Liu, Deheng Zhu, Dongsheng Zhou, Xiaopeng Wei","doi":"10.1186/s42492-026-00224-2","DOIUrl":"10.1186/s42492-026-00224-2","url":null,"abstract":"<p><p>Text-to-motion generation aims to synthesize semantically consistent and naturally coherent motion sequences from natural language descriptions. Given the continuous nature of human motion, diffusion models operating in a continuous latent space offer inherent advantages over vector quantization-based methods, particularly in avoiding quantization errors and in modeling quality. However, existing diffusion models primarily rely on mean squared error loss. This stepwise regression paradigm often leads to 'over-smoothed' motion sequences and struggles to capture the subtle semantic nuances embedded in textual descriptions. To realize the potential for continuous diffusion generation, an enhanced latent-space diffusion framework designed to elevate generation capabilities across two dimensions, namely, distribution approximation and semantic alignment, is proposed. Specifically, a latent-space adversarial discriminator is incorporated. By applying decoupled adversarial supervision, this component mitigates the detail loss caused by mean regression, significantly enhancing the physical realism and dynamic sharpness. Concurrently, a latent-space contrastive alignment strategy is introduced during the denoising process that reinforces the correspondence of the generated motion sequences with the given textual inputs via explicit cross-modal constraints. Extensive experiments on standard benchmarks demonstrate that the proposed method effectively addresses the limitations of conventional diffusion models, thus validating the potential of continuous diffusion frameworks within the domain of text-driven motion synthesis.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey of the intersection of mental maps and visualization.","authors":"Jie Guo, Huiyuan Shi, Yucheng Kong, Jiazhen Du, Zeyang Liu, Fangfang Zhou, Ying Zhao","doi":"10.1186/s42492-026-00225-1","DOIUrl":"10.1186/s42492-026-00225-1","url":null,"abstract":"<p><p>The intersection between mental maps and visualization has attracted increasing research attention in recent years, as they exhibit a natural functional complementarity. Mental maps research explores how humans construct in-mind representations of the external world through the view of cognitive science, while visualization can transform implicit mental maps into concrete visual forms, thereby facilitating the analysis of their construction mechanisms. Visualization, a branch of computer science, investigates how to design human-friendly visual representations of digital data. Mental maps can provide human-perspective insights to guide visualization designs and improve the effectiveness of data understanding and analysis. This paper presents a literature survey of the recent research at this intersection. Regarding the visualizations of mental maps, the present study classifies relevant research into visualizations to depict structural topographies, attribute information in mental maps, and compare multiple mental maps. In terms of mental maps for visualization, this study categorizes the relevant research into the element structuring, structural mapping, and consistency maintenance mechanisms of mental maps to guide visualization designs. This paper introduces and discusses in detail representative works in these categories, providing future research opportunities at this intersection.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZACP: enhancing skin lesion classification ability using zero attention and complete perception.","authors":"Hailong Zuo, Zhi Weng, Zhenshuai Fu, Fangfang Ding, Jun Chai, Liru Chen, Caili Gong, Zhiqiang Zheng","doi":"10.1186/s42492-026-00223-3","DOIUrl":"10.1186/s42492-026-00223-3","url":null,"abstract":"<p><p>With the increasing number of dermatology patients owing to ecological deterioration and unhealthy lifestyles, hospitals are experiencing significant diagnostic pressure, and patients are suffering from delayed treatment and poor medical experience. Therefore, there is an urgent need to develop accurate skin lesion classification tools. To address this issue, this study proposed the zero attention and complete perception (ZACP), a novel skin lesion classification system based on EfficientNet (EN). ZACP integrates two key modules: ZA and CP. ZA is an improved multi-head attention mechanism that injects zero vectors into key/value vectors, introduces a benchmark to suppress invalid attention to non-semantic regions (e.g., normal skin texture), and enhances the focus on lesions. Derived from the squeeze and excitation (SE) module, the CP embeds the ZA to achieve multiscale attention-retaining SE channel-weighting ability while enabling the ZA to capture local subtle features (e.g., melanoma boundaries) via patch segmentation and positional encoding. Additionally, the original activation function of EN was replaced with Mish to stabilize the training. Evaluated on the authoritative HAM10000 and ISIC2017 datasets, ZACP outperformed the comparative models in terms of accuracy (ACC) and other metrics, achieving 95% and 91% ACC on the two datasets, respectively. The source code is available at: http://github.com/ZACP-DERMATOLOGY .</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148391849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRR-Net: a correlation reconstruction and refinement network for deformable medical image registration.","authors":"Bingxian Xie, Guimei Zhang, Ke Xu","doi":"10.1186/s42492-026-00222-4","DOIUrl":"10.1186/s42492-026-00222-4","url":null,"abstract":"<p><p>Deformable image registration is a critical task in medical image analysis. However, existing learning-based methods encounter difficulties in achieving high registration accuracy under large anatomical deformations while maintaining model interpretability. To overcome these limitations, a correlation reconstruction and refinement network (CRR-Net) was introduced, representing the first framework to incorporate super-resolution reconstruction at the feature level for deformable registration. The core innovation lies in the correlation reconstruction and refinement module (CRRM), which enables precise modeling of spatial correspondences by leveraging high-resolution feature spaces. This design captures richer structural details and contextual cues while expanding the receptive field during resolution recovery, thereby improving performance in large-deformation scenarios. Integrated within a pyramid registration framework, the CRRM supports a multi-scale coarse-to-fine strategy based on local correlation modeling, ensuring consistent deformation-field prediction across scales. Model interpretability was further enhanced through a hierarchical visualization of the deformation fields, providing an intuitive quality assessment. Extensive experiments on brain and cardiac datasets demonstrated that CRR-Net outperforms state-of-the-art deformable registration approaches. For example, it achieved comparable performance on the LPBA40 dataset while using 32% fewer parameters and running 31% faster than CorrMLP, a representative high-performance method. This code is publicly available at https://github.com/miracledrumstick/CRR-Net .</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13291313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms.","authors":"Yingqian Zhang, Zhiming Shao, Zechen Wei, Changfu Liu, Zeyu Sun, Liangliang Liu, Ran Xin, Yong Ge, Chen Wang, Guodong Ding, Lei Gao, Liwei Zhang, Hui Hui, Jie Tian, Yundai Chen","doi":"10.1186/s42492-026-00221-5","DOIUrl":"10.1186/s42492-026-00221-5","url":null,"abstract":"<p><p>Coronary heart disease (CAD) is the leading cause of death worldwide, and coronary angiography (CAG) serves as the gold standard for its assessment. Valvular heart diseases, such as severe aortic stenosis (AS) and severe mitral regurgitation (MR), frequently coexist with CAD yet are often underdiagnosed. Opportunistic screening for these conditions at the time of CAG could influence therapeutic strategies and improve prognosis. This study developed and validated a foundation model for the automated screening of severe AS and severe MR from CAG videos. The study presents CAGFound, a video-based foundation model that was self-supervised pre-trained on CAG sequences from seven medical centers and subsequently adapted to two downstream tasks: screening for severe AS and severe MR. Two internal and external validation datasets were retrospectively enrolled from the First Medical Center and the Sixth Medical Center of Chinese PLA General Hospital, respectively. A total of 117,383 unlabeled CAG sequences were used to build CAGFound. For the detection of severe AS, CAGFound achieved an area under the receiver operating characteristic curve (AUROC) of 0.932 (sensitivity 0.767, specificity 0.921) on the internal test dataset and maintained robust performance on the external validation dataset, with an AUROC of 0.879 (sensitivity 0.800, specificity 0.955). For the detection of severe MR, the model demonstrated an AUROC of 0.933 (sensitivity 0.738, specificity 0.938) on the internal dataset and an AUROC of 0.896 (sensitivity 0.754, specificity 0.855) on the external cohort. The performance of CAGFound was also compared with other video-based foundation models, VideoMAEv2 and Video Swin. CAGFound achieved the highest AUROC and demonstrated the best calibration performance (Brier score 0.122, R² 0.478) compared with VideoMAEv2 (Brier score 0.159, R² 0.306) and Video Swin (Brier score 0.162, R² 0.306). CAGFound enables accurate, automated screening for severe AS and severe MR during CAG. It has the potential to increase detection rates, facilitate timely clinical referral, and improve prognosis without requiring additional contrast administration or procedures.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13275965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale feature fusion for few-shot medical image learning with fisher information-driven layer selection.","authors":"Kai Zhang, Yanjun Peng, Bo Pang, Xue Chen","doi":"10.1186/s42492-026-00220-6","DOIUrl":"10.1186/s42492-026-00220-6","url":null,"abstract":"<p><p>Few-shot medical image classification is a highly challenging problem in computer-aided diagnosis, with the central difficulty being enabling deep models to learn discriminative features conducive to classification from limited labeled samples. Vision transformers (ViTs) have recently demonstrated outstanding performance across various visual tasks. However, owing to their large parameter counts and dependence on massive pretraining data, ViTs are prone to overfitting in sample-scarce scenarios typical of few-shot learning. Parameter-efficient fine-tuning (PEFT) techniques, such as low-rank adaptation (LoRA), have alleviated some of these issues. However, conventional PEFT approaches still encounter difficulties in complex medical image classification tasks. To address this, this study proposes a general fine-tuning framework called a hierarchical probing and fusion network (HPF-Net), which integrates three core innovations to allow smarter and more efficient adaptation for few-shot medical image classification. First, a Fisher information-driven layer selection strategy strengthens the layer-selection robustness in few-shot settings. Subsequently, the attention-guided multiscale fusion module aligns and improves the features drawn from the selected critical layers. Subsequently, LoRA is incorporated into this efficient fine-tuning pipeline to reduce the parameter overhead while improving the accuracy. Extensive experiments on the public few-shot medical image benchmark, the medical imaging meta-dataset, demonstrated that HPF-Net significantly outperformed baseline methods, and ablation studies validated the necessity of each proposed component. The source code will be released upon acceptance.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148057257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEDI-SLATE: medical imaging slide-lecture aligned teaching ensemble.","authors":"Motaleb Hossen Manik, Zabirul Islam, Ge Wang","doi":"10.1186/s42492-026-00218-0","DOIUrl":"10.1186/s42492-026-00218-0","url":null,"abstract":"<p><p>Slide-based lectures remain the primary means by which undergraduate students learn about the mathematical, physical, and systems-level foundations of medical imaging. However, despite their central educational role, no openly available dataset pairs imaging lecture slides with clean, well-aligned explanatory narration suitable for scientific and educational research. The authors introduced MEDI-SLATE: medical imaging slide-lecture aligned teaching ensemble, constructed from a complete undergraduate biomedical engineering medical imaging course. The dataset contains 1117 high-resolution slides paired with refined narration derived from classroom audio through automatic speech recognition, followed by careful manual cleanup. MEDI-SLATE encompasses linear systems, Fourier analysis, signal processing, X-ray physics, computed tomography, positron emission tomography/single photon emission computed tomography, magnetic resonance imaging , ultrasound, and optical imaging. In addition to the slide-text pairs, the dataset includes lecture-level difficulty tags, key ideas, common student misunderstandings, and practice questions sourced directly from the instructor's materials. A fully reproducible preprocessing pipeline covering slide extraction, narration refinement, alignment, and corpus-level analyses is provided. MEDI-SLATE offers a high-fidelity, openly available resource for medical imaging education, curriculum development, multimodal learning research, and creation of artificial intelligence-assisted instructional tools, with all data and codes released for transparent use and future extension.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13079244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of complex non-uniform rational B-spline volume parametric models with G¹ continuity.","authors":"Dan Wang, Long Chen, Jiahong Zhang","doi":"10.1186/s42492-026-00217-1","DOIUrl":"10.1186/s42492-026-00217-1","url":null,"abstract":"<p><p>The construction of complex volumetric parametric models has long been a bottleneck in achieving integrated design and simulation modeling. To enhance model quality and simplify the modeling process, this paper proposes an innovative method for improving the continuity of complex volumetric parametric models. First, depending on whether the input consists of design parameters or surface models, a <math> <mrow><msup><mi>G</mi> <mn>0</mn></msup> <mo>/</mo> <msup><mi>C</mi> <mn>0</mn></msup> </mrow> </math> -continuous volumetric parametric model is generated using either a creation or recreation approach. A new data structure is also developed to store the control-point indices and their topological relationships. Next, based on the actual connections among different patches in the volumetric models generated using the two aforementioned methods, the continuity conditions for three different scenarios are formulated. For each scenario, the corresponding systems of equations are established to determine the control points. Subsequently, an algorithm is developed to automatically sort, store, and adjust the relevant control points so that the volumetric parametric model satisfies the G¹ continuity condition. The generated examples demonstrate that the volumetric parametric modeling method proposed in this study is effective for constructing complex models, significantly improving the model quality and rendering them more suitable for subsequent analysis and processing.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13066061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of electroencephalography and electromyography research in robotics: opportunities and challenges.","authors":"Zefeng Wang, Meiyan Xu, Junfeng Yao, Yue Yu, Bingbing Hu, Yufei Wang, Yu Wang, Xiaopeng Zhang","doi":"10.1186/s42492-026-00216-2","DOIUrl":"10.1186/s42492-026-00216-2","url":null,"abstract":"<p><p>In the evolving nexus of neuroscience and robotics, the symbiotic fusion of electroencephalography (EEG) and electromyography (EMG) is emerging as a paradigm-shifting avenue for enhancing human-machine interfaces. While EEG, which captures the subtle electrical nuances of the brain, offers a potent channel for nuanced brain-machine communication, EMG serves as a bridge, converting neuromuscular intentions into actionable directives for robotic apparatuses. This review highlights the current methodologies in which EEG and EMG not only function in silos but also converge harmoniously to dictate robotic control. By delving deeper into this, the intricate synergy between cognitive processes, muscular responses, and machine actions can be unraveled. Subsequently, the discourse also navigates through the myriad challenges encountered in realizing real-time, seamless integration of these bio-signals with robotics and the innovative solutions poised to address them. The aim is to provide a comprehensive understanding of the interplay between neuroscience and robotics. This insight will help drive breakthroughs in adaptive human-machine collaboration.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147487626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion.","authors":"Zhirong Li, Guangfeng Lin, Dou Zhang, Rongxin Huang, Jing Yang","doi":"10.1186/s42492-026-00215-3","DOIUrl":"10.1186/s42492-026-00215-3","url":null,"abstract":"<p><p>Medical imaging technology has advanced rapidly in recent years; however, abnormalities in medical images are often rare and complex, making sample labels difficult to obtain for supervised learning of detection models. Existing unsupervised anomaly detection methods, which are the mainstream approaches, often struggle with issues such as blurred edges and varying scales of abnormal regions. To address these issues, a novel unsupervised method for medical image anomaly detection is proposed: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion (UMIAD-EGMF). This method excavates rich edge information with scale adaptation and progressively identifies discriminative information for anomaly detection. UMIAD-EGMF captures contextual information around anomaly boundaries via low-level feature fusion (enhancing boundary details with the edge guidance module; EGM), integrates EGM-extracted edge information into deeper features using the edge aggregation module, and merges multi-scale feature maps to capture common anomaly features (subtle and significant) through multi-scale flow fusion. Experiments on breast ultrasound images (BUSI), brain magnetic resonance imaging (brain MRI), and head computed tomography (head CT) datasets demonstrate that UMIAD-EGMF outperforms the state-of-the-art methods. Specifically, on the BUSI dataset, the segmentation area under the precision-recall curve for object localization (AUPRO) of UMIAD-EGMF reaches 63.36%, surpassing that of the multi-scale low-level feature enhancement U-Net (MLFEU-net) by 0.01%; on the brain MRI dataset, its segmentation AUPRO is 90.83%, outperforming that of MLFEU-net by 0.33%; and on the head CT dataset, its segmentation AUPRO is 62.24%, exceeding that of MedMAE by 2.37%.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12950837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}