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Segmentation Techniques Applied to CNNs for Cervical Cancer Classification

IEEE Access 2025年 13卷 74678-74688页

作者： Ortiz-Gonzalez, Ana Martinez-Espana, Raquel Morales-Garcia, Juan Imbernon, Baldomero Martinez-Mas, Jose Alvarez, Mauricio A. Romero, Oscar D. Martinez-Cendan, Juan Pedro Bueno-Crespo, Andres University Hospital Complex of Cartagena Pathology Department Spain University of Murcia Information and Communications Engineering Department Spain University of Alicante Department of Software and Computing Systems Spain Spain Catholic University of Murcia Faculty of Medicine Spain CIAGO Gynecological Center Obstetrics and Gynecology Department Spain University of Manchester Computer Science Department United Kingdom University General Hospital Santa Lucía Obstetrics and Gynecology Department Spain

Cervical cancer continues to be a significant global health issue, ranking as the fourth most prevalent cancer affecting women. Enhancing population screening programs by refining the examination of cervical samples conducted by skilled pathologists offers a compelling alternative for early detection of this disease. Deep Learning facilitates the development of automatic classification models to aid experts in this task. However, it is increasingly important to bring explainability to the model both to understand how the network learns to identify pathology and to bring confidence to the diagnosis. In this paper, we design an automatic segmentation masks for the classification of cervicovaginal cell images. This automatic segmentation is combined in a classification model that allows the models to improve their performance thanks to the morphological information provided by the combined segmentation in a Global Average Pooling layer with the convolutional network analysis of the original image. The models will be trained with real data so that learning can recognize the diversity of colors, shapes and sizes of human cell nuclei. The results show a robust and explainable model with satisfactory results, obtaining an F1 Score value of 0.935 in binary classification of revisable and non-revisable cell. © 2013 IEEE.

关键词： Convolutional neural networks

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Neural-Network-Based Control for Discrete-Time Nonlinear systems with Input Saturation Under Stochastic Communication Protocol

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 766-778页

作者： Xueli Wang Derui Ding Hongli Dong Xian-Ming Zhang Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Software and Electrical Engineering Swinburne University of TechnologyMelbourne 3122VictoriaAustralia Institute of Complex Systems and Advanced Control Northeast Petroleum UniversityDaqing 163318China

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input *** save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient *** virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN ***,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical ***,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov ***,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Adaptive dynamic programming(ADP) constrained inputs neural network(NN) stochastic communication protocols(SCPs) suboptimal control

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Dimensions of order in engineering design organizations

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Design Studies 2003年第4期24卷 357-373页

作者： Dias, W.P.S. Subrahmanian, E. Monarch, I.A. Department of Civil Engineering University of Moratuwa Moratuwa Sri Lanka Inst. for Complex Engineered Systems Carnegie Mellon University Pittsburgh PA 15213 United States Software Engineering Institute Carnegie Mellon University Pittsburgh PA 15213 United States

Design practice and artifacts in engineering design organizations can be ordered along four orthogonal dimensions, namely (i) the principle of ordering (top down vs bottom up), (ii) the structure of ordering (aggregation-decomposition vs generalization-specialization), (iii) the breadth of ordering (diversity vs parsimony) and (iv) the perspective of ordering (horizontal vs historical). Four case histories of design organizations are used to illustrate the above and also to demonstrate the tension (either in conjunction or in cyclic sequence) and/or balance between the extremes of the above dimensions. Mechanisms for creating a balance or tension between bottom up generation and top down influence are proposed. © 2002 Elsevier Science Ltd. All rights reserved.

关键词： Computer aided engineering

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complex Adaptive systems 1

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丛书名： Understanding complex systems

1000年

作者： Ted Carmichael Mirsad Hadžikadić Andrew J. Collins

ISBN: (数字)9783030203092

ISBN: (纸本)9783030203078

This book emerged out of international conferences organized as part of the AAAI Fall Symposia series, and the Swarmfest 2017 conference. It brings together researchers from diverse fields studying these complex systems using CAS and agent-based modeling tools and techniques. In the past, the knowledge gained in each domain has largely remained exclusive to that domain. By bringing together scholars who study these phenomena, the book takes knowledge from one domain to provide insight into others.;Most interesting phenomena in natural and social systems include constant transitions and oscillations among their various phases – wars, companies, societies, markets, and humans rarely stay in a stable, predictable state for long. Randomness, power laws, and human behavior ensure that the future is both unknown and challenging. How do events unfold? When do they take hold? Why do some initial events cause an avalanche while others do not? What characterizes these events? What are the thresholds that differentiate a sea change from a non-event?;complex adaptive systems (CAS) have proven to be a powerful tool for exploring these and other related phenomena. The authors characterize a general CAS model as having a large number of self-similar agents that: 1) utilize one or more levels of feedback; 2) exhibit emergent properties and self-organization; and 3) produce non-linear dynamic behavior. Advances in modeling and computing technology have led not only to a deeper understanding of complex systems in many areas, but they have also raised the possibility that similar fundamental principles may be at work across these systems, even though the underlying principles may manifest themselves differently.

关键词： complexity Applications of Nonlinear Dynamics and Chaos Theory Applications of Graph Theory and complex Networks

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Evolutionary Computation and Reinforcement Learning for Cyber-physical System Design

Evolutionary Computation and Reinforcement Learning for Cybe...

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International Conference on software engineering - Companion, ICSE, Companion

作者： Chengjie Lu Department of Engineering Complex Software Systems Simula Research Laboratory Oslo Norway

Cyber-physical systems (CPSs) are designed to in-tegrate computation and physical processes through constantly interacting with the physical environment. The complexity and uncertainty of the environment often come up with unpredictable situations, which place high demands on the dynamic adaptability of CPSs. Further, as the environment evolves, the CPS needs to constantly evolve itself to adapt to the changing environment. This paper presents a research plan that aims to develop a novel framework to address CPS design challenges under uncertain environments. We propose to utilize evolutionary computation and reinforcement learning techniques to design control policies that can adapt to the dynamic changes and uncertainties of the environment. Further, novel testing and evaluation approaches that can generate test cases while adapting to dynamic changes in the system and the environment will be explored.

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Test Scenario Generation for Autonomous Driving systems with Reinforcement Learning

Test Scenario Generation for Autonomous Driving Systems with...

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International Conference on software engineering - Companion, ICSE, Companion

作者： Chengjie Lu Department of Engineering Complex Software Systems Simula Research Laboratory Oslo Norway

We have seen rapid development of autonomous driving systems (ADSs) in recent years. These systems place high requirements on safety and reliability for their mass adoption, and ADS testing is one of the crucial approaches to ensure the success of ADSs. To this end, this paper presents RLTester, a novel ADS testing approach, which adopts reinforcement learning (RL) to learn critical environment configurations (i.e., test scenarios) of the operating environment of ADSs that could reveal their unsafe behaviors. To generate diverse and critical test scenarios, we defined 142 environment configuration actions, and adopted the Time- To-Collision metric to construct the reward function. Our evaluation shows that RLTester discovered a total of 256 collisions, of which 192 are unique collisions, and took on average 11.59 seconds for each collision. Further, RLTester is effective in generating more diverse test scenarios compared to a state-of-the art approach, DeepCollision.

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Steering dynamic collaborations between business processes

Steering dynamic collaborations between business processes

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作者： Zhao, Xiaohui Liu, Chengfei Information Systems Group Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology 5600 Eindhoven Netherlands Centre for Complex Software Systems and Services Faculty of Information and Communication Technologies Swinburne University of Technology Melbourne VIC 3122 Australia

Under the background of business globalization nowadays, many organizations are connecting their business processes into complex collaborative business processes to facilitate business collaboration. To adapt to the changing requirements and market opportunities, collaborative business processes have to evolve all the time. Such dynamics brings challenges to the modeling and tracking of collaborative business processes. In addition, these issues can be further complicated by the requirements of privacy protection and information openness in the interorganizational context. Aiming to tackle these problems, this paper proposes a comprehensive framework to model and track dynamic collaborative business processes with our extended relative workflow model. This framework helps each participating organization derive its collaborative business processes and update the processes at run time. A process visibility control mechanism is developed to enforce such derivation and extend organizations' process perception via process visibility transitivity. The whole framework is formalized with matrices and is theoretically proven to be privacy safe. Corresponding algorithms are developed for generating and tracking collaborative business processes. A prototype is also implemented for the proof-of-concept purpose. © 2010 IEEE.

关键词： Process control

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Network and Parallel Computing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Jian Cao Minglu Li Min-You Wu Jinjun Chen

ISBN: (数字)9783540881407

ISBN: (纸本)9783540881391

Welcome to the proceedings of the 2008 IFIP International Conference on Network and Parallel Computing (NPC 2008) held in Shanghai, China. NPC has been a premier conference that has brought together researchers and pr- titioners from academia, industry and governments around the world to advance the theories and technologies of network and parallel computing. The goal of NPC is to establish an international forum for researchers and practitioners to present their - cellent ideas and experiences in all system fields of network and parallel computing. The main focus of NPC 2008 was on the most critical areas of network and parallel computing, network technologies, network applications, network and parallel archit- tures, and parallel and distributed software. In total, the conference received more than 140 papers from researchers and prac- tioners. Each paper was reviewed by at least two internationally renowned referees and selected based on its originality, significance, correctness, relevance, and clarity of presentation. Among the high-quality submissions, only 32 regular papers were accepted by the conferences. All of the selected conference papers are included in the conference proceedings. After the conference, some high-quality papers will be r- ommended to be published in the special issue of international journals. We were delighted to host three well-known international scholars offering the k- note speeches, Sajal K. Das from University Texas at Arlington USA, Matt Mutka from Michigan State University and David Hung-Chang Du from University of M- nesota University of Minnesota.

关键词： Computer Communication Networks Computer systems Organization and Communication Networks Computer engineering Data Structures and Information Theory Models and Principles Cryptology

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Network and parallel computing: IFIP international conference, NPC 2008 Shanghai, China, october 18-20, 2008 proceedings

Network and parallel computing: IFIP international conferenc...

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IFIP International Conference on Network and Parallel Computing, NPC 2008

作者： Cao, Jian Li, Minglu Wu, Ming-You Chen, Jingjun Shanghai Jiatong University Department of Computer Science and Engineering 80 Dongcuan Road Shanghai China Swinburne University of Technology Centre for Complex Software Systems & Services 1 Alfred Street Hawthorn MelbourneVIC Australia

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An embedded co-AdaBoost and its application in classification of software document relation

An embedded co-AdaBoost and its application in classificatio...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Liu, Jin Li, Juan Xie, Yuan Lei, Jeff Hu, Qiping State Key Lab. of Software Engineering Computer School Wuhan University China State Key Lab. of Intelligent Control and Management of Complex Systems Institute of Automation CAS China Department of Computer Science and Engineering University of Texas Arlington TX United States International School of Software Wuhan University China

ISBN: (纸本)9780769547947

To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learning into the Adaboost learning framework and at the same time leverages the advantages of Co-training algorithm for performance enhancement. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of the convergence rate, the accuracy, and the steady performance as compared to the original AdaBoost algorithm, without relying on redundant and sufficient feature sets. As a algorithm application in software engineering, the Embedded Co-AdaBoost has been applied to the classification of software document relations to improve the quality of the architecture design documents and the reusability of design knowledge. © 2012 IEEE.

关键词： Reusability

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