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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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Contrastive Continual learning with Importance Sampling and Prototype-Instance Relation Distillation

arXiv

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arXiv 2024年

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates AI Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China Department of CSE The Hong Kong University of Science and Technology China

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Predicting Response to Patients with Gastric Cancer Via a Dynamic-Aware Model with Longitudinal Liquid Biopsy data

SSRN

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SSRN 2024年

作者： Chen, Zifan Zhao, Jie Li, Yanyan Li, Yilin Liu, Huimin Feng, Xujiao Nan, Xinyu Dong, Bin Shen, Lin Chen, Yang Zhang, Li Center for Data Science Peking University Beijing China Department of Gastrointestinal Oncology 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 Guangzhou Medical University Guangzhou China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China

Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have become recognized as a valuable data modality, offering essential cellular and molecular insights and enabling the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, which included 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates the dynamic data through AI-engineered components, enabling an in-depth longitudinal analysis. Utilizing three-fold cross-validation, DAM exhibited superior performance over traditional cell-counting-based methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic focus-area-related visual features and their strong association with treatment response. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with ***: This work was supported by the National Natural Science Foundation of China (81801778 to Li Zhang., 82203881 to Yang Chen, U22A20327 to Lin Shen, 12090022 to Bin Dong), Beijing Natural Science Foundation (7222021 to Yang Chen), Beijing Hospitals Authority Youth Programme (QML20231115 to Yang Chen), Clinical Medicine Plus X-Young Scholars Project of P

关键词： Diseases

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Natural Convection Optimizer: A Novel Physics-Based Metaheuristic Optimization Algorithm

SSRN

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SSRN 2024年

作者： Liang, Kang Yipeng, Wang Jiayi, Chen Krakhmalev, Oleg Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Foreign Languages and CulturalCommunication Shanghai Polytechnic University Shanghai201209 China School of Economics and Management Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Rus-sian Federation 4‐th Veshnyakovsky Passage 4 Moscow109456 Russia

In this paper, Natural Convection Optimizer Algorithm (NCvO) is proposed as a novel physical-inspired metaheuristic optimization algorithm. It is a novel physics-inspired metaheuristic optimization algorithm based on natural convection phenomena in fluid dynamics. By simulating the heat transfer process in fluids, NCvO achieves a balanced exploration and exploitation of complex search spaces. We validate NCvO through three experiments: solving the CEC 2017 benchmark in 10 dimensions and comparing results with well-established metaheuristics using statistical analyses like Wilcoxon and Friedman tests. NCvO demonstrates superior performance in terms of average fitness values and objective function evaluations, establishing its potential as a robust optimization tool. © 2024, The Authors. All rights reserved.

关键词： Optimization

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Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference machines

arXiv

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arXiv 2023年

作者： Adam, Alexandre Perreault-Levasseur, Laurence Hezaveh, Yashar Welling, Max Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Microsoft Research AI4Science

Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the Recurrent Inference machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Methodology for Applying IIoT Architecture to Design IIOT Systems in the Agricultural Sector of the Russian Federation

Methodology for Applying IIoT Architecture to Design IIOT Sy...

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Information Computing and Processing (ICP), Seminar on

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

The article presents the results of the study of Industrial Internet of Things (IIoT) technologies for the agricultural sector, taking into account international experience for the period 2004 - 2021, and proposes a methodology for applying IIoT architecture to design IIOT systems in the agricultural sector of Russia. A two-factor classification of IIoT systems in agriculture has been made. A typical conceptual scheme of intelligent IIoT system for viticulture and winemaking has been developed. The architecture of a typical IIoT system is analyzed, taking into account the observance of information security in data processing and transmission. An analysis of the current state and prospects for the implementation of IoT in the Russian wine industry has been carried out. A business process model is presented in the concept of BPME (BPM Everywhere) for the production of a certain brand of wine. The model in a non-decomposed form shows sub-processes with and without IoT, using traditional “manual” methods, as well as taking into account the impact of various business processes on production efficiency and product quality. The reasons for the lag and growth drivers for the use of IIoT in agrosector are identified.

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Object Modeling in Kinematic Problems of Manipulation Robots

SSRN

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SSRN 2023年

作者： Liang, Kang Krakhmalev, Oleg Blagoveshchensky, Ivan Krakhmalev, Nikita Beiresh, Andrew Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia 4-th Veshnyakovsky Passage 4 Moscow109456 Russia Volokolamsk highway building 11 Moscow125080 Russia Department of Engineering Graphics Moscow State University of Technology "STANKIN" Vadkovsky Lane 3a Moscow127055 Russia

A method for compiling object diagrams has been developed to describe algorithms for calculating the kinematic parameters of manipulation robots. Examples of drawing up object diagrams for calculating the speeds and accelerations of points selected on the manipulation robot links are considered. Analysing the computational complexity of the obtained algorithms is carried out and a method is proposed to increase their computational efficiency. Raising the computational efficiency is achieved by reducing the addition and multiplication operations performed by the algorithm. A two-level parallel computational algorithm is developed, which makes it possible to additionally improve the computational efficiency when using multiprocessor computing systems. Information and computational graphs illustrating the developed algorithms are presented. The conducted research allows automating the process of modelling the manipulative robot movement. The object approach and the modelling methods carried out on its basis can also be implemented in the control systems for manipulation robots. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

arXiv

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arXiv 2023年

作者： Payot, Nicolas Pasquato, Mario Trani, Alessandro Alberto Hezaveh, Yashar Perreault-Levasseur, Laurence Département de Physique Université de Montréal Mila Quebec Artificial Intelligence Institute Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Niels Bohr Institute Copenhagen Denmark Research Center for the Early Universe School of Science The University of Tokyo Tokyo Japan Okinawa Institute of Science and Technology Okinawa Japan

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem’s decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology. © 2023, CC BY.

关键词： Fractals

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Predicting High School Students' Academic Performance: A Comparative Study of Supervised machine learning Techniques

Predicting High School Students' Academic Performance: A Com...

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machine learning-Driven Digital Technologies for Educational Innovation Workshop

作者： Nadia N. Sánchez-Pozo Juan S. Mejía-Ordóñez Diana C. Chamorro Dagoberto Mayorca-Torres Diego H. Peluffo-Ordóñez Machine learning Research Program SDAS Research Group Ben Guerir Morocco Carrera de Ingeniería Química Faculted de Ciencias Químicas y de la Salud Universidad Técnica de Machala Machala Ecuador Programa de Mecatrónica Facultad de Ingeniería Universidad Mariana Pasto Colombia Modeling Simulation and Data Analysis (MSDA) Research Program Mohamed VI Polytechnique University Ben Guerir Morocco

ISBN: (纸本)9781665427647

The proliferation of mobile devices and the rapid development of information and communication technologies have revolutionized education. Educational data has evolved to be voluminously massive, broadly various, and produced at high velocity. Therefore, computerized techniques for integrating, processing, and transforming data into valuable knowledge have become necessary to improve internal academic processes. Specifically, educational data mining is an emerging discipline concerned with analyzing the massive amounts of academic data generated and stored by educational institutions. In this sense, machine learning algorithms aid decision-makers who are establishing strategies to improve students' learning experience and institutional effectiveness by revealing hidden patterns in academic performance. Thus, this paper describes our comparative study of machine learning techniques to predict academic performance. We selected the features that best fit the discovery of patterns in the academic performance of high school students, resulting in a balance between accuracy and interpretability. We implemented six supervised learning algorithms for pattern recognition: Light Gradient Boosting machine, Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and K-nearest Neighbors. The experimental results showed that the Gradient Boosting (Gbc) algorithm achieved the highest accuracy (96.77%), superior to other classification techniques considered.

关键词： Technological innovation machine learning algorithms Social networking (online) Predictive models Boosting Prediction algorithms Classification algorithms

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Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Medicine Plus

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Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

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