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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

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A Novel Black Box Process Quality optimization Approach based on Hit Rate

arXiv

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arXiv 2023年

作者： Yang, Yang Wu, Jian Song, Xiangman Wu, Derun Su, Lijie Tang, Lixin Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Manufacturing Department of Shanghai Meishan Iron & Steel Co. LTD. Jiangsu Nanjing210039 China Frontier Science Center for Industrial Intelligence and Systems Optimization

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

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Biomechanical models in the lower-limb exoskeletons development: A review

TechRxiv

引用

TechRxiv 2023年

作者： Firouzi, Vahid Seyfarth, Andre Song, Seungmoon Von Stryk, Oskar Sharbafi, Maziar Ahmad Lauflabor Locomotion Laboratory Institute of Sport Science TU Darmstadt Germany Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Germany Mechanical and Industrial Engineering Northeastern University United States

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This review explores the applications of biomechanical gait models in the development of lower-limb exoskeletons. Biomechanical models can be broadly categorized as conceptual and detailed models. Biomechanical models can be used for the design, control, and assessment of exoskeletons. Also, these models can be used to estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. The analysis of applications and research areas aims to inspire further advancements in this field and contribute to the development of more effective exoskeletons. We highlight validating simulation studies, improving the accuracy and fidelity of biomechanical models, and benefiting from exoskeletons to enhance or validate biomechanical models as future research areas related to biomechanical models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. Also, increasing the availability of open-source resources accelerates exoskeleton and biomechanical model advancement. Our review reveals existing needs and cracks new perspectives for the development of more usable and human-compatible exoskeletons based on biomechanical models. © 2023, CC BY.

关键词： Exoskeleton (Robotics)

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GPU-Accelerated Privacy Aggregation Queries of Crowdsensing Spatio-temporal Data

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IEEE Transactions on Consumer Electronics 2024年

作者： Li, Yuxi Chen, Jingjing Ji, Dong Zhu, Fa Chen, Xingchi Park, Jong Hyuk Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Nanjing Forestry University College of Information Sciences and Technology College of Artificial Intelligence Nanjing China Pengcheng Laboratory Shenzhen518000 China Department of Computer Science and Engineering 232 Gongneung-ro Seoul Korea Republic of

Addressing the challenges of query privacy leakage and high verification costs in multisource spatio-temporal data queries within crowdsensing environments, we present CrowdPQ-a novel privacy-preserving aggregation query scheme for spatio-temporal crowdsensing data utilizing GPU acceleration. Our approach leverages a robust two-server secure architecture. At the core of our scheme lies a privacy query protocol based on Function Secret Sharing (FSS), ensuring confidentiality while enabling efficient data aggregation queries. Additionally, we propose a non-interactive lightweight verification protocol utilizing Beaver multiplication triples, significantly minimizing resource consumption from invalid query tokens. We provide a comprehensive formal analysis of CrowdPQ in terms of correctness, security, and complexity. Our prototype, implemented in a two-server setting with GPU support, demonstrates superior performance: GPU acceleration reduces query processing time by 4×, server-side communication cost by 25×, and client-side communication by up to 88.89% compared with existing methods. These results highlight the potential of GPU-enabled secure processing for large-scale crowdsensing data aggregation tasks, offering an innovative pathway for privacy-focused data handling in complex environments. © 1975-2011 IEEE.

关键词： Spatio-temporal data

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What will regularized continuous learning performs if it was used to medical image segmentation: a preliminary analysis

What will regularized continuous learning performs if it was...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weihao Dai Chaolu Feng Shuaizheng Chen Wei Li Jinzhu Yang Dazhe Zhao College of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image (MIIC) Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.

关键词：

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Multi-residual 2D network integrating spatial correlation for whole heart segmentation

引用

computers in Biology and Medicine 2024年 172卷 108261-108261页

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Li, Honghe Hou, Yang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder–decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://***/nancy1984yan/MultiResNet-SC. © 2024 Elsevier Ltd

关键词： Graphics processing unit

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SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning

arXiv

引用

arXiv 2024年

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaiane, Osmar Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art (SOTA) methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Su, Junming Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada

The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a challenge: pseudo-labels generated by image registration come with significant noise. To address this issue, we propose a self-paced sample selection framework (SPSS) for BSS. Specifically, SPSS comprises two main components: 1) self-paced uncertainty sample selection (SU) for explicitly improving the quality of pseudo labels in the image space, and 2) self-paced bidirectional feature contrastive learning (SC) for implicitly improving the quality of pseudo labels through enhancing the separability between class semantics in the feature space. Both SU and SC are trained collaboratively in a self-paced learning manner, ensuring that SPSS can learn from high-quality pseudo labels for BSS. Extensive experiments on two public medical image segmentation datasets demonstrate the effectiveness and superiority of SPSS over the state-of-the-art. Our code is release at https://***/SuuuJM/SPSS. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Multi-agent Traffic Prediction via Denoised Endpoint Distrib...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yao Liu Ruoyu Wang Yuanjiang Cao Quan Z. Sheng Lina Yao Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatiotemporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and un-certainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

关键词： Uncertainty Navigation Soft sensors Robot kinematics Predictive models Transformers Road traffic Trajectory Complexity theory Spatiotemporal phenomena

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Few-Shot Small Vessel Segmentation Using a Detail-Preserving Network Enhanced by Discriminator

SSRN

引用

SSRN 2024年

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Hou, Yang Shang, Jin Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases. © 2024, The Authors. All rights reserved.

关键词： Semi-supervised learning

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