Annotated Dataset Research Articles

Use Self-Training Random Forest for Predicting Winter Wheat Yield

The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wheat varieties during the 2019–2020 and 2021–2022 growing seasons. The testing results indicated that our model achieved an R2 of 0.84, RMSE of 627.94 kg/ha, and MAE of 516.94 kg/ha in the test dataset, while the validation dataset yielded an R2 of 0.81, RMSE of 692.96 kg/ha, and MAE of 550.62 kg/ha. In comparison, the standard random forest resulted in an R2 of 0.81, RMSE of 681.02 kg/ha, and MAE of 568.97 kg/ha in the test dataset, with validation results of an R2 of 0.79, RMSE of 736.24 kg/ha, and MAE of 585.85 kg/ha. Overall, these results demonstrate that our self-training random forest algorithm is a practical and effective solution for expanding annotated datasets, thereby enhancing the prediction accuracy of ML models in winter wheat yield forecasting.

Preserving Fairness in AI under Domain Shift

Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and then fielded for utilization. This training strategy is effective in problems with stationary distributions, where both the training and testing data are drawn from the same distribution. However, it is vulnerable with respect to distributional shifts in the input space that may occur after the initial training phase. As a result, the time-dependent nature of data can introduce biases and performance degradation into the model predictions, even if the model is initially fair. Model retraining from scratch using a new annotated dataset is a naive solution that is expensive and time-consuming. We develop an algorithm to adapt a fair model to remain fair and generalizable under domain shift using solely new unannotated data points. We recast this learning setting as an unsupervised domain adaptation (UDA) problem. Our algorithm is based on updating the model such that the internal representation of data remains unbiased despite distributional shifts in the input space. We provide empirical validation on three common fairness datasets to show that the challenge exists in practical setting and to demonstrate the effectiveness of our algorithm.

“Double vaccinated, 5G boosted!”: Learning Attitudes Towards COVID-19 Vaccination from Social Media

The sudden onset of the recently concluded COVID-19 pandemic has driven substantial progress in various scientific fields. One notable example is the comprehension of public vaccination attitudes and the timely monitoring of their fluctuations through social media platforms. This approach can serve as a cost-effective means to supplement surveys in gathering public vaccine hesitancy levels. In this paper, we propose a deep-learning framework leveraging textual posts on social media to extract and track users’ vaccination stances in near real-time. Compared to previous works, we integrate into the framework the recent posts of a user’s social network friends to collaboratively detect the user’s genuine attitude towards vaccination. Based on our annotated dataset from X (previously known as Twitter), the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to the state-of-the-art text-only models. Using this framework, we successfully confirm the feasibility of using social media to track the evolution of vaccination attitudes in real life. In addition, we illustrate the generality of our framework in extracting other public opinions such as political ideology. We further show one practical use of our framework by validating the possibility of forecasting a user’s vaccine hesitancy changes with information perceived from social media.

Whombat: An open‐source audio annotation tool for machine learning assisted bioacoustics

Abstract Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high‐stakes applications, such as conservation and scientific research, demands a data‐centric approach with a focus on selecting and utilizing carefully annotated and curated evaluation and training data that are relevant and representative. Creating annotated bioacoustic datasets presents a number of challenges, such as managing large collections of recordings with associated metadata, developing flexible annotation tools that can accommodate the diverse range of vocalization profiles of different organisms and addressing the scarcity of expert annotators. We present Whombat, a user‐friendly, browser‐based interface for managing audio recordings and annotation projects, with several visualization, exploration and annotation tools. It enables users to quickly annotate, review, and share annotations, as well as visualize and evaluate a set of machine learning predictions on a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: (1) a project aimed at enhancing automated UK bat call identification at the Bat Conservation Trust (BCT), and (2) a collaborative effort among the USDA Forest Service and Oregon State University researchers exploring bioacoustic applications and extending automated avian classification models in the Pacific Northwest, USA. Whombat is a flexible tool that can effectively address the challenges of annotation for bioacoustic research. It can be used for individual and collaborative work, hosted on a shared server or accessed remotely, or run on a personal computer without the need for coding skills. The code is open‐source, and we provide a user guide.

Tracing the Displacement of Data Work in AI: A Political Economy of “Human-in-the-Loop”

In this study, we trace the evolution of a data work team in an artificial intelligence (AI) startup in India. By bringing attention to data work, which is the indispensable work of preparing annotated datasets for training AI systems, conducted within a formal organisational set up, we underline: 1. how organisational approaches adopted to balance investor and client preferences shape work arrangements and spatial division of the data workers; 2. how relations between the data team and the ‘core’ technical team serve to invisibilise human labour in the production of AI; and 3. how increasing codification of data work leads to devaluation of data work within the organisation and deskilling of young data workers at large, making them vulnerable in choosing a meaningful career path of their choice. In tracing this trajectory of displacement of data workers employed in a formal sector, we show that the prevalent characterisation of data work as being invisible or precarious is not inherent to AI nor inevitable in its labour processes. Rather, it is produced through the specific embedding of AI production within the political economy of startup capitalism. Through this, we seek to recentre the discourse on AI and future-of-work away from deterministic projections of AI’s impact on work and towards the specific labour processes of AI and its implications for the skills and career trajectories of a young and growing workforce in the Global South.

A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors

This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. There is a notable gap in systematic analyses of foundation models for specialized detection tasks, particularly within the domain of facial accessories. Six state-of-the-art models—Grounding DINO, Detic, OWLViT, OWLv2, YOLO World, and Florence-2—were evaluated across three datasets (FFHQ with custom annotations, CelebAMask-HQ, and Face Synthetics) to assess their effectiveness in zero-shot detection and labeling. Performance metrics, including Average Precision (AP), Average Recall (AR), and Intersection over Union (IoU), were used to benchmark foundation models. The results show that Detic achieved the highest performance scores (AP of 0.97 and AR of 0.98 on FFHQ, with IoU values reaching 0.97), making it highly suitable for automated annotation workflows. Grounding DINO and OWLv2 also showed potential, especially in high-recall scenarios. The results emphasize the importance of prompt engineering. Practical recommendations for using foundation models in specialized dataset annotation are provided.

Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images

AbstractWe report a novel machine‐learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI data set from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current ML classification benchmark. The outcome of this work will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible. To demonstrate possible ways to utilize this database, we performed a statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set.

Robustness of a DenseNet-121 for the Classification of ARDS in Chest X-Rays

Abstract Research in the field of artificial intelligence (AI) in medicine is increasingly relying on algorithms based on deep learning (DL), especially for radiology. Despite producing promising results, DL models have a major drawback: their reliance on large training datasets. Especially in medicine, large, annotated datasets are hard to obtain, leading to low robustness and a performance loss when exposed to unseen, new data. To address this problem, our research evaluates how well data augmentation is able to expand the used dataset and thus improve a DL model. We employ 17 different augmentation methods to test the robustness of a DenseNet-121 trained to classify Acute Respiratory Distress Syndrome (ARDS) in chest X-rays. Our experiments show that while the model has low robustness for augmented test data when trained on unaugmented data, the general performance for ARDS classification can be improved by augmenting the training data. Overall, this demonstrates that data augmentation is beneficial in training AI models for ARDS classification in order to create more robust and generalizable models.

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation

Background:Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods. Methods:We develop a multi-class labeling system based on guidelines from FDA, CDC, and WHO, distinguishing between six categories of suicidal behaviors and allowing for multiple labels per data sample. To efficiently create an annotated dataset, we use an MPNet-based semantic retrieval framework to extract relevant sentences from a large EHR dataset, reducing annotation space while capturing diverse expressions. Experts annotate the extracted sentences using the multi-class system. We then formulate the task as a multi-label classification problem and fine-tune transformer-based models on the curated dataset to accurately classify suicidal behaviors in EHRs. Results:Lexical analysis revealed key themes in assessing suicide risk, considering an individual’s history, mental health, substance use, and family background. Fine-tuned transformer-based models effectively identified suicidal behaviors from EHRs, with Bio_ClinicalBERT, BioBERT, and XLNet achieving the F1 scores (0.81), outperforming BERT and RoBERTa. The proposed approach, based on a multi-label classification system, captures the complexity of suicidal behaviors effectively particularly “Suicide Attempt” and “Family History” instances. The proposed approach, using task-specific NLP models and a multi-label classification system, captures the complexity of suicidal behaviors more effectively than traditional binary classification. However, direct comparisons with existing studies are difficult due to varying metrics and label definitions. Conclusion:This study presents a robust NLP framework for detecting suicidal behaviors in EHRs, leveraging task-specific fine-tuning of transformer-based models and a semi-automated pipeline. Despite limitations, the approach demonstrates the potential of advanced NLP techniques in enhancing the identification of suicidal behaviors. Future work should focus on model expansion and integration to further improve patient care and clinical decision-making.

PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain.

The production of food, feed, fiber, and fuel is a key task of agriculture, which has to cope with many challenges in the upcoming decades, e.g., a higher demand, climate change, lack of workers, and the availability of arable land. Vision systems can support making better and more sustainable field management decisions, but also support the breeding of new crop varieties by allowing temporally dense and reproducible measurements. Recently, agricultural robotics got an increasing interest in the vision and robotics communities since it is a promising avenue for coping with the aforementioned lack of workers and enabling more sustainable production. While large datasets and benchmarks in other domains are readily available and enable significant progress, agricultural datasets and benchmarks are comparably rare. We present an annotated dataset and benchmarks for the semantic interpretation of real agricultural fields. Our dataset recorded with a UAV provides high-quality, pixel-wise annotations of crops and weeds, but also crop leaf instances at the same time. Furthermore, we provide benchmarks for various tasks on a hidden test set comprised of different fields: known fields covered by the training data and a completely unseen field.

Development of a code-switched Hindi-Marathi dataset and transformer-based architecture for enhanced speech recognition using dynamic switching algorithms

Especially in low-resource languages like Hindi and Marathi, code-switching presents major difficulties for automatic speech recognition (ASR). This work provides a 450-hour annotated dataset of Hindi-Marathi code-switching, including tag-switching, intra-sentential, and inter-sentential patterns. We augment a transformer-based ASR architecture with dynamic switching algorithms using wav2vec2 and a reinforcement learning-based approach known as Q-Learning, thereby dynamically optimizing language transition points.With a Word Error Rate (WER) of 0.2800 and a Character Error Rate (CER) of 0.2400, the proposed model beats conventional HMM-GMM and RNN-based ASR systems. Combining reinforcement learning for dynamic code-switching with transformer-based self-supervised learning demonstrates enhanced accuracy and flexibility.Comparative analysis shows the improvements relative to heuristic methods, Kaldi baselines, and pre-trained monolingual models. This work underscores the significance of hybrid architectures, dynamic algorithms, and sophisticated acoustic modeling in code-switched speech recognition, thereby offering a comprehensive framework for multilingual automatic speech recognition. The results have a major impact on the evolution of ASR in linguistically diverse and economically constrained environments.

A composite scaling network of EfficientNet for improving spatial domain identification performance

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.

Efficient class‐agnostic obstacle detection for UAV‐assisted waterway inspection systems

AbstractEnsuring the safety of water airport runways is essential for the correct operation of seaplane flights. Among other tasks, airport operators must identify and remove various objects that may have drifted into the runway area. In this paper, the authors propose a complete and embedded‐friendly waterway obstacle detection pipeline that runs on a camera‐equipped drone. This system uses a class‐agnostic version of the YOLOv7 detector, which is capable of detecting objects regardless of its class. Additionally, through the usage of the GPS data of the drone and camera parameters, the location of the objects are pinpointed with 0.58 m Distance Root Mean Square. In our own annotated dataset, the system is capable of generating alerts for detected objects with a recall of 0.833 and a precision of 1.

HindiSumm: A Hindi Abstractive Summarization Benchmark Dataset

Abstractive Text Summarization (ATS) is a task to create a novel summary by generating fresh sentences incorporating new words or rephrasing the article. It is a complex task as the model needs to understand the semantic similarity between the sentences of the text. To fulfill this, there is a need for a large annotated benchmark dataset, which is available for resource-rich languages such as English and non-indic languages. In contrast, for the less-resourced languages, such as Indic languages, the available datasets are limited and involve very short summary sentences. Hence, a language-specific abstractive summarization dataset called HindiSumm was introduced for Hindi, consisting of 570,000 text-summary pairs from Navbharat Times across 21 domains. The HindiSumm dataset’s efficiency is evaluated extrinsically and intrinsically by using various metrics. Furthermore, two recent multilingual-cased pre-trained models are fine-tuned on the HindiSumm dataset individually. In addition, an ensembled approach using weighted averaging is also incorporated to check the efficacy of the proposed dataset. The model is tested with the in-house created dataset, and results are evaluated on ROUGE scores and show significant improvements of around 13.2% for the proposed HindiSumm compared with other benchmark datasets. In the future, the HindiSumm dataset will promote the progress of ATS for the Indian language.

Segmentation of MRI tumors and pelvic anatomy via cGAN-synthesized data and attention-enhanced U-Net

Accurate tumor segmentation within MRI images is of great importance for both diagnosis and treatment; however, in many cases, sufficient annotated datasets may not be available. This paper develops a novel approach to the medical image segmentation of tumors in the brain, liver, and pelvic regions within MRI images, by combining an attention-enhanced U-Net model with a cGAN. We introduce three key novelties: a patch discriminator in the cGAN to enhance realism of generated images, attention mechanisms in the U-Net to enhance the accuracy of segmentation, and finally an application to pelvic MRI segmentation, which has seen little exploration. Our method addresses the issue of limited availability of annotated data by generating realistic synthetic images to augment the process of training. Our experimental results on brain, liver, and pelvic MRI datasets show that our approach outperforms the state-of-the-art methods with a Dice Coefficient of 98.61 % for brain MRI, 88.60 % for liver MRI, and 91.93 % for pelvic MRI. We can also observe great increases in the Hausdorff Distance, at especially complex anatomical regions such as tumor boundaries. The proposed combination of synthetic data creation and novel segmentation techniques opens new perspectives for robust medical image segmentation.

Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models.

Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO). The AL framework selectively identifies the most informative unlabeled instances for expert annotation, minimizing labeling costs while optimizing classifier performance. PSO, a nature-inspired metaheuristic algorithm, enhances the selection process within AL, ensuring the most relevant data points are chosen. This method was applied to train multiple Convolutional Neural Network (CNN) models on the HAM10000 skin lesion dataset. Experimental results demonstrate that the proposed AL-PSO approach significantly improves classification accuracy, with the Least Confidence strategy achieving approximately 89.4% accuracy while using only 40% of the labeled training data. This represents a substantial improvement over traditional approaches in terms of both accuracy and efficiency. The findings indicate that the integration of AL and PSO can accelerate the adoption of AI in clinical settings for skin cancer detection. The code for this study is publicly available at ( https://github.com/Sayantani-31/AL-PSO ).

An open annotated dataset and baseline machine learning model for segmentation of vertebrae with metastatic bone lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions. In addition to raw images, data include manual segmentations and contours, vertebral level labeling, vertebral lesion-type classifications, and patient demographic details. Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. This data will facilitate the development and validation of models for predicting the mechanical response to loading and the resulting risk of fractures and spinal deformity.

Person in Uniforms Re-Identification

Person in Uniforms Re-identification (PU-ReID) is an emerging computer vision task for various intelligent video surveillance applications. PU-ReID is much understudied due to the absence of large-scale annotated datasets, also this task is extremely challenging because many individuals captured in surveillance videos wear same clothing, introducing significant interference for retrieval tasks owing to the high visual similarity of outfits and subtle differences among individuals. This research initiates the exploration of person in uniform re-identification, a novel and challenging task tailored for real industrial scenarios. To address these issues, a novel framework is proposed for PU-ReID, which aims to reduce the visual impact of similar uniforms and learn the unique cues derived from human parts and detailed visual features. Specifically, several novel techniques are built in this study: first, a uniform feature separation method with orthogonal constraints is proposed to extract non-uniform features. Second, multi-view subspace feature alignment is introduced to integrate soft-biometrics including optics-related visual features, contextual information of human parts, and cloth-invariant biometric features. In addition, to close the gap between academic research and real world settings, a new person in uniforms ReID dataset named PU-151 is constructed, which consists of 151 gas station employees in uniforms from 1,488 videos. At last, extensive experiments conducted on five datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art methods. This advancement can drive further developments in re-identification and person search technologies.

Comparative analysis of vision transformers and fine-tuned transfer learning models for brain tumor classification

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.