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Medical Image Registration and Its Application in Retinal Images: A Review

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

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arXiv 2023年

作者： Yuan, Han Hong, Chuan Tran, Nguyen Tuan Anh Xu, Xinxing Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space – the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum and we refer to this area as "lung+ space". While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive. Methods We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung+ space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Results Our results demonstrated significant improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD). These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyperparameter selection to validate the stability of our method. Conclusion The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the signi

关键词： Domain Knowledge

A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation

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arXiv 2022年

作者： Wu, Huizi Fang, Hui Sun, Zhu Geng, Cong Kong, Xinyu Ong, Yew-Soon Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China A*STAR Institute of High Performance Computing Centre for Frontier AI Research Singapore Ant Group China A*STAR Centre for Frontier AI Research Nanyang Technological University Singapore

Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve recommendation performance, where deep learning methods account for the majority. However, most of these methods are black-box ones and ignore to provide moderate explanations to facilitate users’ understanding, which thus might lead to lowered user satisfaction and reduced system revenues. Therefore, in our study, we propose a generic Reinforced Explainable framework with Knowledge graph for Session-based recommendation (i.e., REKS), which strives to improve the existing black-box SR models (denoted as non-explainable ones) with Markov decision process. In particular, we construct a knowledge graph with session behaviors and treat SR models as part of the policy network of Markov decision process. Based on our particularly designed state vector, reward strategy, and loss function, the reinforcement learning (RL)-based framework not only achieves improved recommendation accuracy, but also provides appropriate explanations at the same time. Finally, we instantiate the REKS in five representative, state-of-the-art SR models (i.e., GRU4REC, NARM, SR-GNN, GCSAN, BERT4REC), whereby extensive experiments towards these methods on four datasets demonstrate the effectiveness of our framework on both recommendation and explanation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations

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arXiv 2024年

作者： Shao, Yumeng Li, Jun Shi, Long Wei, Kang Ding, Ming Li, Qianmu Li, Zengxiang Chen, Wen Jin, Shi The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO Sydney Australia The Digital Economy Research Institute Nanjing University of Science and Technology Jiangsu Nanjing210094 China The Institute of High Performance Computing A*STAR Singapore The Digital Research Institute ENN Group China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Conventional synchronous federated learning (SFL) frameworks suffer from performance degradation in heterogeneous systems due to imbalanced local data size and diverse computing power on the client side. To address this problem, asynchronous FL (AFL) and semi-asynchronous FL have been proposed to recover the performance loss by allowing asynchronous aggregation. However, asynchronous aggregation incurs a new problem of inconsistency between local updates and global updates. Motivated by the issues of conventional SFL and AFL, we first propose a time-driven SFL (T-SFL) framework for heterogeneous systems. The core idea of T-SFL is that the server aggregates the models from different clients, each with varying numbers of iterations, at regular time intervals. To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function. Further, we optimize the aggregation weights to minimize the developed upper bound. Then, we develop a discriminative model selection (DMS) algorithm that removes local models from clients whose number of iterations falls below a predetermined threshold. In particular, this algorithm ensures that each client’s aggregation weight accurately reflects its true contribution to the global model update, thereby improving the efficiency and robustness of the system. To validate the effectiveness of T-SFL with the DMS algorithm, we conduct extensive experiments using several popular datasets including MNIST, Cifar-10, Fashion-MNIST, and SVHN. The experimental results demonstrate that T-SFL with the DMS algorithm can reduce the latency of conventional SFL by 50%, while achieving an average 3% improvement in learning accuracy over state-of-the-art AFL algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

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arXiv 2024年

作者： Yuan, Han Hong, Chuan Jiang, Pengtao Zhao, Gangming Tran, Nguyen Tuan Anh Xu, Xinxing Yan, Yet Yen Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States College of Computer Science Nankai University China Faculty of Engineering The University of Hong Kong Hong Kong Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Radiology Changi General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template’s boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. Conclusions: In the context of pneumothorax diagnos

关键词： Diagnosis

Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits

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Physical Review Letters 2022年第13期129卷 133601-133601页

作者： Shichuan Xue Yizhi Wang Junwei Zhan Yaxuan Wang Ru Zeng Jiangfang Ding Weixu Shi Yong Liu Yingwen Liu Anqi Huang Guangyao Huang Chunlin Yu Dongyang Wang Xiang Fu Xiaogang Qiang Ping Xu Mingtang Deng Xuejun Yang Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China China Greatwall Research Institute China Greatwall Technology Group CO. LTD. Shenzhen 518057 China National Innovation Institute of Defense Technology AMS Beijing 100071 China

Quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4n to 2n−m), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.

关键词： Integrated optics Quantum entanglement Quantum tomography Tomography Variational approach

A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

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A Novel Deep Learning Approach Featuring Graph-Based Algorit...

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches

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arXiv 2023年

作者： Li, Siqi Miao, Di Wu, Qiming Hong, Chuan D’Agostino, Danny Li, Xin Ning, Yilin Shang, Yuqing Fu, Huazhu Ong, Marcus Eng Hock Haddadi, Hamed Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Computing Imperial College London London United Kingdom Institute of Data Science National University of Singapore Singapore Singapore

Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications. © 2023, CC BY.

关键词： Decision making

MACHINE LEARNING APPLICATIONS IN ARCHAEOLOGICAL PRACTICES: A REVIEW

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arXiv 2025年

作者： Bellat, Mathias Orellana Figueroa, Jordy D. Reeves, Jonathan S. Taghizadeh-Mehrjardi, Ruhollah Tennie, Claudio Scholten, Thomas Collaborative Research Center 1070 "ResourceCultures" University of Tübingen Tübingen72074 Germany Department of Geosciences Working Group Soil Science and Geomorphology University of Tübingen Tübingen72074 Germany High Performance and Cloud Computing Group University of Tübingen Tübingen72074 Germany Technological Primates Research Group Max Planck Institute for Evolutionary Anthropology Leipzig04103 Germany Faculty of Agriculture Natural Resources Ardakan University Ardakan Iran Department of Geosciences Working Group Early Prehistory and Quaternary Ecology University of Tübingen Tübingen72070 Germany DFG Cluster of Excellence "Machine Learning: New Perspectives for Science" University of Tübingen Tübingen72076 Germany

Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods from prehistory to modern times. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of relevant publications from 2019 onwards, mainly concentrated in a few journals and in majority published in open access format. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, archaeological predictive modelling, and architectural classification or reconstruction. From the corpus of articles analysed, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning models are gaining in popularity they remain subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we propose here a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and available data. As in many areas of modern life, machine learning is rapidly becoming an important tool in archaeological research and practice, particularly useful for the analyses of large and highly multivariate data, although not without limitations. This review highlights the importance of well-define

关键词： Adversarial machine learning