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IEEE International Symposium on Biomedical Imaging

作者： Heyun Chen Zifan Chen Jie Zhao Haoshen Li Jiazheng Li Yiting Liu Mingze Yuan Peng Bao Xinyu Nan Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Peking University Cancer Hospital&Institute China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate segmentation of gastric tumors is critical yet presents a formidable challenge in medical imaging, where conventional UNet-based frameworks, despite their prevalence, falter on intricate tumor samples due to their limited interactive capacities. The SAM-based segmentation methods address this shortcoming yet with insufficient accuracy. By ingeniously blending images with mask inputs, our MSI-UNet leverages a U-shaped design to deliver pixel-perfect segmentation accuracy, while a novel multi-scale attention module adeptly harnesses interaction points for refined information extraction. When benchmarked on gastric tumor segmentation tasks, MSI-UNet surpasses existing state-of-the-art methods, elevating the Dice Similarity Coefficient (DSC) from 74.82% to 79.3% and minimizing Average Surface Distance (ASD) from 6.46 to 1.98, achieving a comparable accuracy with inter-radiologist consistency of 79.7% DSC. Furthermore, our framework demonstrates superior predictive prowess in survival analysis, enhancing the C-index from 61.7% to 68.68%. Ample experimental comparisons have substantiated that MSI-UNet holds the potential to offer considerable assistance to healthcare professionals in managing and decoding subsequent medical procedures.

关键词： Image segmentation Accuracy Three-dimensional displays Medical services Predictive models Information retrieval Decoding

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

arXiv

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arXiv 2023年

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Jörg K.H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Helmholtz Imaging DKFZ Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Interactive Machine Learning Group DKFZ Applied Computer Vision Lab DKFZ Machine Learning Lab University of Freiburg Freiburg Germany London United Kingdom Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications. © 2023, CC BY.

关键词： Network layers

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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Overview of Modern Forecasting Methods and Models 16

Overview of Modern Forecasting Methods and Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Tsvirkun, Anatoliy Sunchalina, Anna Goroshnikova, Tatiana Sunchalin, Andrey Zholobova, Galina Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of the Development of Large-Scale Systems Moscow Russia Financial University under the Government of the Russian Federation Faculty of International Economic Relations Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods. © 2023 IEEE.

ISBN: (纸本)9798350337907

关键词： Neural networks

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An Evolutionary machine learning Approach to Outlier Detec-Tion Based on Weighted Sum Model with Automatic Weight Discovery by Slsl-Qpso

SSRN

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SSRN 2024年

作者： Kang, Liang Zhang, Xiukai Krakhmalev, Oleg Zeng, Wenlei Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 4‐th Veshnyakovsky Passage 4 Moscow109456 Russia

In this study, we present a weighted sum methodology to assign a quantifiable value to each data point within a dataset, reflecting its propensity to be an outlier. This value is subsequently trans-formed into a probability via a sigmoid function, denoting the likelihood of the data point being an outlier. However, manually crafting weights for the weighted sum is impractical due to their var-iable significance across different datasets. To address this, an evolutionary optimization algorithm is used, namely Short-Lived Swarm-Layer Quantum-behaved Particle Swarm Optimization (SLSL-QPSO), which is an advanced variant of particle swarm optimization, to autonomously learn these weights. The learning process is guided by a fitness function derived from binary cross-entropy loss. We evaluate the algorithm’s performance across 14 meticulously chosen datasets, which differ in data point distribution, feature count, and outlier proportion. The empirical results demonstrate robust performance on these datasets, suggesting the algorithm’s potential to make contributions to the field. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

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BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation

arXiv

引用

arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Hu, Miao Wu, Di Liao, Pengshan Guizani, Mohsen Sheng, Michael The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Developing a Strategy for the Digital Transformation of an Educational Organization Based on Cloud Technology

Developing a Strategy for the Digital Transformation of an E...

引用

Information Systems Theory and Practice (ISTP), Seminar on

作者： Alexander V. Olifirov Krystina A. Makoveichuk Sergei A. Petrenko Department of Economics and Finance V.I Vernadsky Crimean Federal University Yalta Russia Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Department of Information Security Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

The article examines the theory and practical implementation of a digital transformation strategy. It was found that the main stages of formation of the digital transformation strategy of the organization are determined by: the use of digital services, individualization of a particular instance of the service, the involvement of the end consumer in the process, customer support throughout the life cycle of the service. Six main stages of implementation of the strategy of digital transformation of the educational process based on the use of cloud technology were formed. Opportunities and threats to the use of cloud technology in the educational space were identified. The experience of using the cloud service “1C: Fresh” in the formation of more than one hundred information bases in the training of specialists in the field of economics and finance was obtained. It was shown that the use of cloud technologies with the provision of students with access to the cloud service of 1C in the educational space of the university simulates the production activities of future specialists in real time. In this case, up-to-date and daily updated for the current legislation programs are used, functionality, cybersecurity and a high degree of data security are maintained at the proper level.

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Correction to: Potential cyber threats of adversarial attacks on autonomous driving models

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Journal of Computer Virology and Hacking Techniques 2023年第2期20卷 315-315页

作者： Boltachev, Eldar Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

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PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images

arXiv

引用

arXiv 2024年

作者： Chen, Zifan Nan, Xinyu Li, Jiazheng Zhao, Jie Li, Haifeng Lin, Ziling Li, Haoshen Chen, Heyun Liu, Yiting Tang, Lei Zhang, Li Dong, Bin Center for Data Science Peking University Beijing China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, limiting their transferability across different tasks or modalities. While general segmentation models offer some versatility in natural image processing, they struggle with the unique characteristics of medical images. There is an urgent need in clinical practice for a new segmentation approach that can effectively handle medical imagery features while maintaining adaptability across various three-dimensional objects and imaging modalities. Methods: We introduce PAM (Propagating Anything Model), a propagation-based segmentation approach that operates on 3D medical image volumes using a 2D prompt (bounding box or sketch mask). PAM extrapolates this initial input to generate a complete 3D segmentation by modeling inter-slice structural relationships, establishing a continuous information flow within 3D medical structures. This approach enhances segmentation effectiveness across various imaging modalities by focusing on structural and semantic continuities rather than isolating specific objects. The model combines a CNN-based UNet architecture for intra-slice information processing with a Transformer-based attention module to facilitate inter-slice propagation. This innovative framework results in a method with unique generalizability, capable of segmenting diverse 3D objects across different medical imaging modalities. Results: PAM demonstrated superior performance on 44 diverse medical datasets, notably improving the dice similarity coefficient (DSC) for hundreds of segmentation object types and various medical imaging modalities. Compared to modern models like MedSAM and SegVol, PAM achieved an average DSC improvement of over 18.1%, while maintaining stable predictions despite prompt deviation (one-way ANOVA test, P ≥ 0.5985) and varying propagation confi

关键词： Semantic Segmentation

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