Reconstruction Loss Research Articles

Deep federated learning hybrid optimization model based on encrypted aligned data

Federated learning can achieve multi-party data-collaborative applications while safeguarding personal privacy. However, the process often leads to a decline in the quality of sample data due to a substantial amount of missing encrypted aligned data, and there is a lack of research on how to improve the model learning effect by increasing the number of samples of encrypted aligned data in federated learning. Therefore, this paper integrates the functional characteristics of deep learning models and proposes a Variational AutoEncoder Gaussian Mixture Model Clustering Vertical Federated Learning Model (VAEGMMC-VFL), which leverages the feature extraction capability of the autoencoder and the clustering and pattern discovery capabilities of Gaussian mixture clustering on diverse datasets to further explore a large number of potentially usable samples. Firstly, the Variational AutoEncoder is used to achieve dimensionality reduction and sample feature reconstruction of high-dimensional data samples. Subsequently, Gaussian mixture clustering is further employed to partition the dataset into multiple potential Gaussian-distributed clusters and filter the sample data using thresholding. Additionally, the paper introduces a labeled sample attribute value finding algorithm to fill in attribute values for encrypted unaligned samples that meet the requirements, allowing for the full recovery of encrypted unaligned data. In the experimental section, the paper selects four sets of datasets from different industries and compares the proposed method with three federated learning clustering methods in terms of clustering loss, reconstruction loss, and other metrics. Tests on precision, accuracy, recall, ROC curve, and F1-score indicate that the proposed method outperforms similar approaches.

S[formula omitted]U-PVNet: Arbitrary-scale point cloud upsampling via Point-Voxel Network based on Siamese Self-Supervised Learning

Recently, benefiting from the rapid development of deep learning, many research on point cloud upsampling task has been successfully proposed with remarkable performance. The quality of the upsampled point clouds is also vital for the quality of the reconstructed meshes in 3D surface reconstruction task. However, most existing methods either independently train a specific upsampling network for each scale factor or heavily rely on the paired training data as the supervision information. To address these limitations which are both inefficient and impractical for storage and computation in real applications, we present a Point-Voxel Network based on Siamese Self-supervised learning for arbitrary-Scale point cloud Upsampling (S3U-PVNet), which mainly includes a Down–Up pipeline and an Up–Down pipeline, to support self-supervised point cloud upsampling with arbitrary scale factors by a single network. The core of our network is a series of stacked point-voxel feature fusion (PVFF) modules, which can effectively and efficiently extract and fuse multi-granularity features from input point clouds. Each module includes two branches, a point-based branch that represents the sparse inputs in points to adaptively learn the spatial relationship among points, and a voxel-based branch that extract features in voxels to reduce the problem of inaccurate feature extraction caused by the irregularity and sparsity of point clouds. Furthermore, we also propose an end-to-end training objective for our siamese self-supervised network encapsulating reconstruction loss and similarity loss, which considers both global shape constraint and local geometric constraint. We achieve new state-of-the-art unsupervised upsampling results on various synthetic datasets and demonstrate the generalization of the proposed method on challenging real-world data.

An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

The investigation of image deblurring techniques in dynamic scenes represents a prominent area of research. Recently, deep learning technology has gained extensive traction within the field of image deblurring methodologies. However, such methods often suffer from limited inherent interconnections across various hierarchical levels, resulting in inadequate receptive fields and suboptimal deblurring outcomes. In U-Net, a more adaptable approach is employed, integrating diverse levels of features effectively. Such design not only significantly reduces the number of parameters but also maintains an acceptable accuracy range. Based on such advantages, an improved U-Net model for enhancing the image deblurring effect was proposed in the present study. Firstly, the model structure was designed, incorporating two key components: the MLFF (multilayer feature fusion) module and the DMRFAB (dense multi-receptive field attention block). The aim of these modules is to improve the feature extraction ability. The MLFF module facilitates the integration of feature information across various layers, while the DMRFAB module, enriched with an attention mechanism, extracts crucial and intricate image details, thereby enhancing the overall information extraction process. Finally, in combination with fast Fourier transform, the FRLF (Frequency Reconstruction Loss Function) was proposed. The FRLF obtains the frequency value of the image by reducing the frequency difference. The present experiment results reveal that the proposed method exhibited higher-quality visual effects. Specifically, for the GoPro dataset, the PSNR (peak signal-to-noise ratio) reached 31.53, while the SSIM (structural similarity index) attained a value of 0.948. Additionally, for the Real Blur dataset, the PSNR achieved 31.32, accompanied by an SSIM score of 0.934.

Brain imaging endophenotypes by unsupervised deep learning

AbstractBackgroundUnderstanding the genetic architecture of brain structure and Alzheimer’s disease is challenging, partly due to difficulties in designing robust, non‐biased descriptors of the brain.MethodWe present approaches to derive robust brain imaging phenotypes using unsupervised deep representation learning. Training a 3‐D convolutional autoencoder model with reconstruction loss, we derived a vector representation (termed endophenotype) that captures rich morphological information of the brain. Further, we developed a perturbation‐based decoder interpretation approach that can highlight brain regions that are most relevant to individual endophenotypes.ResultUsing UK Biobank data, we trained the model using over 6,000 UK Biobank (UKBB) participants’ T1 or T2‐FLAIR (T2) brain MRIs. The model is used to derive 128 endophenotypes. The endophenotypes have a mean heritability of 0.3 and the GWAS of which have identified 43 independent loci in the held‐out UKBiobank dataset (discovery n = 22,962/replication n = 12,848), among which 13 loci have not been previously reported by the UK Biobank Big40 study.ConclusionUsing UK Biobank data, we show that, compared to traditional brain image‐derived phenotypes (IDPs), our endophenotypes are more heritable and have higher power for genetic discovery.

The ROAD to discovery: Machine-learning-driven anomaly detection in radio astronomy spectrograms

Context. As radio telescopes increase in sensitivity and flexibility, so do their complexity and data rates. For this reason, automated system health management approaches are becoming increasingly critical to ensure nominal telescope operations. Aims. We propose a new machine-learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen. To evaluate our method, we present a dataset consisting of 6708 autocorrelation-based spectrograms from the Low Frequency Array (LOFAR) telescope and assign ten different labels relating to the system-wide anomalies from the perspective of telescope operators. This includes electronic failures, miscalibration, solar storms, network and compute hardware errors, among many more. Methods. We demonstrate how a novel self-supervised learning (SSL) paradigm, that utilises both context prediction and reconstruction losses, is effective in learning normal behaviour of the LOFAR telescope. We present the Radio Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based anomaly detection and a supervised classification, thereby enabling both classification of both commonly occurring anomalies and detection of unseen anomalies. Results. We demonstrate that our system works in real time in the context of the LOFAR data processing pipeline, requiring <1ms to process a single spectrogram. Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while maintaining a false positive rate of 2%, as well as a mean per-class classification F-2 score of 0.89, outperforming other related works.

Deep Fuzzy Variable C-Means Clustering Incorporated With Curriculum Learning

End-to-end deep clustering method utilizes deep neural networks to jointly learn representation features and clustering assignments. Although many k-means-friendly deep clustering models have been explored, the existing division-based methods tend to directly implement clustering with a specific number of clusters, which suffers from poor performance resulted from indistinguishable clusters, and contributes to bad local optimum. At the same time, the representation learning of fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering in the feature space still needs more research. In this paper, a Deep Fuzzy Curriculum Clustering (DFC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) method with the learning strategy of clustering from easy to complex automatically is proposed to tackle above issues. Firstly, considering the soft flexible allocation of fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means and the preservation of local structure of original data, the fuzzy clustering loss and the autoencoder's reconstruction loss are constructed to learn the embedded features and clustering centers simultaneously. Second, curriculum loss is introduced into the constraint to make clusters successively merge in line with implementing clustering from easy to complex, and realize the bottom-up deep aggregative clustering automatically. In addition, novel curriculum information is proposed as constraint to guide the merging of clusters belonging to the same class. Experimental results on four real-world datasets show the superiority of the proposal.

Unsupervised Continual Learning in Streaming Environments.

A deep clustering network (DCN) is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This article presents an unsupervised approach of DCN construction on the fly via simultaneous deep learning and clustering termed autonomous DCN (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer, while the final output is inferred via the summation of cluster prediction score. Furthermore, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared with its counterparts while offering fully autonomous construction of ADCN structure in streaming environments in the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in https://github.com/andriash001/AutonomousDCN.git.

On the Power of Gradual Network Alignment Using Dual-Perception Similarities.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

Unsupervised Multimodal Anomaly Detection With Missing Sources for Liquid Rocket Engine.

To achieve reliable and automatic anomaly detection (AD) for large equipment such as liquid rocket engine (LRE), multisource data are commonly manipulated in deep learning pipelines. However, current AD methods mainly aim at single source or single modality, whereas existing multimodal methods cannot effectively cope with a common issue, modality incompleteness. To this end, we propose an unsupervised multimodal method for AD with missing sources in LRE system. The proposed method handles intramodality fusion, intermodality fusion, and decision fusion in a unified framework composed of multiple deep autoencoders (AEs) and a skip-connected AE. Specifically, the first module restores missing sources to construct a complete modality, thus advancing the secondary reconstruction. Different from vanilla reconstruction-based methods, the proposed method minimizes reconstruction loss and meanwhile maximizes the dissimilarity of representations in two latent spaces. Utilizing reconstruction errors and latent representation discrepancy, the anomaly score is acquired. At decision level, the model performance can be further enhanced via anomaly score fusion. To demonstrate the effectiveness, extensive experiments are carried out on multivariate time-series data from static ignition of several LREs. The results indicate the superiority and potential of the proposed method for AD with missing sources for LRE.

SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder.

Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx and initiating timely clinical intervention has important implications for reducing complications caused by re-irradiation. This research informs clinical decision-making by making predictions on re-irradiation of recurrent nasopharyngeal carcinoma using deep learning multi-modal information fusion between multi-sequence nuclear magnetic resonance imaging and plan dose. Specifically, we assume that the hidden variables of model data can be divided into two categories: task-consistency and task-inconsistency. The task-consistency variables are characteristic variables contributing to target tasks, while the task-inconsistency variables are not apparently helpful. These modal characteristics are adaptively fused when the relevant tasks are expressed through the construction of supervised classification loss and self-supervised reconstruction loss. The cooperation of supervised classification loss and self-supervised reconstruction loss simultaneously reserves the information of characteristic space and controls potential interference simultaneously. Finally, multi-modal fusion effectively fuses information through an adaptive linking module. We evaluated this method on a multi-center dataset. and found the prediction based on multi-modal features fusion outperformed predictions based on single-modal, partial modal fusion or traditional machine learning methods.

A graph embedding based fault detection framework for process systems with multi-variate time-series datasets

Due to the enormous potential of modelling, graph-based approaches have been used for various applications in the process industries. In this study, we propose a fault detection framework through graphs by utilising its attributes in the form of node embeddings. Shallow embedding methods are deployed to generate node embedding vectors. Shallow embedding methods are broadly classified into matrix factorisation and skip-gram-based methods. Node2vec and Deepwalk fall under skip-gram models, while GraphRep and HOPE constitute the Matrix factorisation methods. Node embedding values generated from these methods are then fed to the variational auto-encoder, which ranks the nodes in reconstruction loss value. The node embedding reconstruction loss values exceeding a particular threshold are considered outliers. The proposed work has been validated on NPCIL power-flux data and the benchmark Tennessee Eastman data. The results indicate that skip-gram models, especially Node2vec-VAE, outperformed the matrix factorisation methods for both the above-mentioned datasets.

Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks

Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was shown to improve model performance even within small datasets whilst data harmonisation often led to lower model generalisability, which was unaffected using the SHRED technique. We show that our proposed model produces site-invariant biomarkers, most notably the connection between transverse temporal gyrus and paracentral lobule. Site-specific salient FC features were also elucidated, especially implicating the paracentral lobule for our local dataset. Our examination of these salient FC features demonstrates how site-specific features and site-invariant biomarkers can be differentiated, which can deepen our understanding of the neurobiology of schizophrenia.

Smoke veil prior regularized surgical field desmoking without paired in-vivo data

Though deep learning-based surgical smoke removal methods have shown significant improvements in effectiveness and efficiency, the lack of paired smoke and smoke-free images in real surgical scenarios limits the performance of these methods. Therefore, methods that can achieve good generalization performance without paired in-vivo data are in high demand. In this work, we propose a smoke veil prior regularized two-stage smoke removal framework based on the physical model of smoke image formation. More precisely, in the first stage, we leverage a reconstruction loss, a consistency loss and a smoke veil prior-based regularization term to perform fully supervised training on a synthetic paired image dataset. Then a self-supervised training stage is deployed on the real smoke images, where only the consistency loss and the smoke veil prior-based loss are minimized. Experiments show that the proposed method outperforms the state-of-the-art ones on synthetic dataset. The average PSNR, SSIM and RMSE values are 21.99±2.34, 0.9001±0.0252 and 0.2151±0.0643, respectively. The qualitative visual inspection on real dataset further demonstrates the effectiveness of the proposed method.

A semi-supervised high-quality pseudo labels algorithm based on multi-constraint optimization for speech deception detection

Deep learning-based speech deception detection research relies on a large amount of labeled data. However, in the process of collecting speech deception detection data, the identification of truth and lies requires researchers to have a professional knowledge reserve, which greatly limits the number of annotated samples. Improving the accuracy of lie detection with insufficient annotation data is the focus of this study at this stage. In this paper, we propose a semi-supervised high-quality pseudo-label algorithm based on multi-constraint optimization (HQPL-MC) for speech deception detection. Firstly, the algorithm exploits the potential feature information of unlabeled data by using deep auto-encoder networks; secondly, it achieves entropy minimization with the help of the pseudo labeling technique to reduce the class overlap distribution of truth and deception data; finally, it improves the quality of pseudo labels by optimizing the unlabeled loss and reconstruction loss to further enhance the classification performance of the model when the labeled data is insufficient. We recorded an interview-style corpus by ourselves and used it in this paper for the experimental demonstration of the algorithm together with the Columbia/SRI/Colorado(CSC) corpus. The detection performance of the proposed algorithm is better than most state-of-the-art algorithms.

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

The increasing prevalence of unknown-type attacks on the Internet highlights the importance of developing efficient intrusion detection systems. While machine learning-based techniques can detect unknown types of attacks, the need for innovative approaches becomes evident, as traditional methods may not be sufficient. In this research, we propose a deep learning-based solution called the log-cosh variational autoencoder (LVAE) to address this challenge. The LVAE inherits the strong modeling abilities of the variational autoencoder (VAE), enabling it to understand complex data distributions and generate reconstructed data. To better simulate discrete features of real attacks and generate unknown types of attacks, we introduce an effective reconstruction loss term utilizing the logarithmic hyperbolic cosine (log-cosh) function in the LVAE. Compared to conventional VAEs, the LVAE shows promising potential in generating data that closely resemble unknown attacks, which is a critical capability for improving the detection rate of unknown attacks. In order to classify the generated unknown data, we employed eight feature extraction and classification techniques. Numerous experiments were conducted using the latest CICIDS2017 dataset, training with varying amounts of real and unknown-type attacks. Our optimal experimental results surpassed several state-of-the-art techniques, achieving accuracy and average F1 scores of 99.89% and 99.83%, respectively. The suggested LVAE strategy also demonstrated outstanding performance in generating unknown attack data. Overall, our work establishes a solid foundation for accurately and efficiently identifying unknown types of attacks, contributing to the advancement of intrusion detection techniques.

Multi-View Masked Autoencoder for General Image Representation

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention for showing state-of-the-art performance on various downstream tasks, though it has shown poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, thus exploiting an image-level approach by learning common features from two different augmented views. We strengthen the MIM by learning long-range global patterns from contrastive loss. Our framework adopts a simple encoder–decoder architecture, thus learning rich and general representations by following a simple process: (1) Two different views are generated from an input image with random masking and by contrastive loss, we can learn the semantic distance of the representations generated by an encoder. By applying a high mask ratio, of 80%, it works as strong augmentation and alleviates the representation collapse problem. (2) With reconstruction loss, the decoder learns to reconstruct an original image from the masked image. We assessed our framework through several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieved 84.3% in fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, thus exceeding previous studies and showing promising results on other downstream tasks. The experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

Monocular depth estimation using self-supervised learning with more effective geometric constraints

Self-supervised learning-based depth estimation from monocular videos is a challenging yet promising way for 3D environment perception. Existing methods that use photometric consistency as supervision are often fragile in the case of textureless environments or illumination variations. In this paper, we adopt geometric constraints to improve the reliability and accuracy of monocular depth estimation. We introduce an equation derived from the two-view imaging geometry, from which we also develop a novel geometric loss function that is shown to be more effective than the epipolar constraint in self-supervised learning. In addition, we design a depth reconstruction loss by explicitly aligning the depth scale of the consecutive estimated depth maps to keep the depth scale consistent. Finally, a new self-supervised learning framework is formulated by incorporating the proposed geometric constraints into depth, pose, and optical flow estimation models. Extensive experiments on KITTI, Make3D, and NYU Depth v2 datasets show that, by adding our geometric constraints, the depth estimation model compares favorably with state-of-the-art self-supervised learning methods, and each sub-task has a significant improvement.

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

The perception system is a safety-critical component that directly impacts the overall safety of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep-learning model used in the perception system. However, studies have shown that these models are highly vulnerable to the adversarial perturbation of input data. The existing works mainly focused on studying the impact of these adversarial attacks on classification rather than regression models. Therefore, this paper first introduces two generalized methods for perturbation-based attacks: (1) We used naturally occurring noises to create perturbations in the input data. (2) We introduce a modified square, HopSkipJump, and decision-based/boundary attack to attack the regression models used in ADSs. Then, we propose a deep-autoencoder-based adversarial attack detector. In addition to offline evaluation metrics (e.g., F1 score and precision, etc.), we introduce an online evaluation framework to evaluate the robustness of the model under attack. The framework considers the reconstruction loss of the deep autoencoder that validates the robustness of the models under attack in an end-to-end fashion at runtime. Our experimental results showed that the proposed adversarial attack detector could detect square, HopSkipJump, and decision-based/boundary attacks with a true positive rate (TPR) of 93%.

Vascular Reconstruction and Limb Loss in Military Tibial Artery Injuries

Selective operative management of injuries to the tibial arteries is controversial, with the necessity of revascularization in the face of multiple tibial arteries debated. Tibial artery injuries are frequently encountered in military trauma, but revascularization practices and outcomes are poorly defined. We aimed to investigate associations between the number of injured vessels and reconstruction and limb loss rates in military casualties with tibial arterial trauma. A US military database of lower extremity vascular injuries from Iraq and Afghanistan (2004-2012) was queried for limbs sustaining at least 1 tibial artery injury. Injury, intervention characteristics, and limb outcomes were analyzed by the number of tibial arteries injured (1, T1; 2, T2; 3, T3). Two hundred twenty one limbs were included (194 T1, 22 T2, 5 T3). The proportions with concomitant venous, orthopedic, nerve, or proximal arterial injuries were similar between groups. Arterial reconstruction (versus ligation) was performed in 29% of T1, 63% of T2, and universally in T3 limbs (P<0.001). Arterial reconstruction was via vein graft (versus localized repair) in 62% of T1, 54% of T2, and 80% of T3 (P=0.59). T3 received greater blood transfusion volume (P=0.02), and fasciotomy was used universally (versus 34% T1 and 14% T2, P=0.05). Amputation rates were 23% for T1, 26% for T2, and 60% for T3 (P=0.16), and amputation was not significantly predicted by arterial ligation in T1 (P=0.08) or T2 (P=0.34) limbs. Limb infection was more common in T3 (80%) than in T1 (25%) or T2 (32%, P=0.02), but other limb complication rates were similar. In this series of military lower extremity injuries, an increasing number of tibial arteries injured was associated with the increasing use of arterial reconstruction. Limbs with all 3 tibial arteries injured had high rates of complex vascular reconstruction and eventual amputation. Limb loss was not predicted by arterial ligation in 1-vessel and 2-vessel injuries, suggesting that selective reconstruction in these cases is advisable.