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SSRN 2024年

作者： Zhang, Xin Wang, Bing Huang, Taifeng Yang, Shuo Tian, Xuedong Zhai, Junhai Yang, Ying College of Mathematics and Information Science Hebei University Wusi Road 180 Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Wusi Road 180 Hebei Baoding071000 China Hebei University Affiliated Hospital Hebei University Wusi Road 180 Hebei Baoding071000 China College of Electronic Information Engineering Hebei University Qiyi Road 2666 Hebei Baoding071000 China Hebei University China

Semi-supervised learning (SSL) has emerged as one of effective strategies to leverage useful information from unlabeled datasets to improve deep model performance, and it is widely utilized in medical image segmentation to alleviate the burden of expensive pixel-level labeling. Most existing SSL-based medical image segmentation methods utilize all unlabeled data equally to update the models through unsupervised loss. However, not all unlabeled data is applicable to the same training process of the model due to varying data quality and importance. In this paper, we propose a new dual-decoder data decoupling training-based semi-supervised medical image segmentation network (DD-Net), allowing the model to focus on challenging regions. DD-Net decouples the prediction of unlabeled data into data with different functions based on the confidence matching degree from two student decoders, and adopts different optimization strategies for different functional data. Specifically, for high-reliability part, we employ the cross pseudo supervision learning to improve the reliability of the model prediction. For divergent prediction, we propose mutual matching learning to guide the model to learn richer information from high uncertainty by assigning high entropy to the prediction data. For the low confidence prediction, we present the reinforcement consistency learning to enhance the model context representations to effectively capture the important details, such as edges and contours, from unlabeled data. Experiments on four medical challenging image datasets show that our method outperforms the previous state-of-the-art methods in the field. The code is available at https://***/TaifengHuang/DD-Net. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Ma-Transunet: U-Shaped Transformer with Multi-Scale Cnn-Based Auxiliary Network for Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Wang, Bing Zhao, Zutong Wei, Zhihong Zhai, Junhai Tian, Xuedong Zhang, Xin Yang, Ying College of Mathematics and Information Science Hebei University Wusi Road 180 Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Wusi Road 180 Hebei Baoding071000 China Hebei University Affiliated Hospital Hebei University Wusi Road 180 Hebei Baoding071000 China College of Electronic Information Engineering Hebei University Qiyi Road 2666 Hebei Baoding071000 China Hebei University China

Recently, pure Transformer-based U-shape networks (PTUs) have achieved significant success in medical image segmentation tasks typically dominated by Convolutional Neural Networks (CNNs), due to their outstanding ability to capture global long-range dependencies at multiple layers. However, they still suffer from limitations in local detail representation such as textures and edges. While some CNN-Transformer complementary approaches leverage the merits of both to obtain higher performance, they either fail to fully exploit the benefits brought by PTU models or adopt too complex feature fusion manners that can reduce network flexibility and limit their practice in clinic. To address these issues, we propose a novel CNN-Transformer combined approach (MA-TransUnet), which is a main-auxiliary separable framework. U-shaped Transformer main network (UTM) captures multi-level global context correlations, and multi-scale CNN-based auxiliary network (MCA) remedies local detail extraction and ensures semantic feature consistency. The separable design style of MA-TransUnet allows it to flexibly adapt to a variety of existing Transformer blocks and structures, facilitating to efficiently build the combined networks based on Transformer and CNN to improve the performance of the whole segmentation models. Besides, we propose a novel multi-scale Transformer block based on efficient attention mechanism to further enhance multi-scale feature extraction for MA-TransUnet network. Extensive experiments on synapse multi-organ and ISIC 2018 skin lesion segmentation datasets demonstrate that the MA-TransUnet surpasses the majority of current methods based solely on CNNs, Transformers, or hybrid CNN-Transformers with good efficiency and generality. Code is available on https://***/Zutong-Zhao/***. © 2024, The Authors. All rights reserved.

关键词： Extraction

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A Deep Model Towards Accurate Boundary Location and Strong Generalization for Medical Image Segmentation

SSRN

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SSRN 2023年

作者： Wang, Bing Geng, Peipei Li, Tianxu Yang, Ying Tian, Xuedong Zhang, Guochun Zhang, Xin College of Mathematics and Information Science Hebei University Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Hebei Baoding071000 China Hebei University Affiliated Hospital Hebei Baoding071000 China College of Cyber Security and Computer Hebei University Hebei Baoding071000 China College of Electronic Information Engineering Hebei University Hebei Baoding071000 China

Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis and monitoring. However, due to the complexity of medical images and the limitations of image acquisition, most of the current segmentation models still face the challenges of imprecise boundary positioning and poor generalization ability, which greatly affect their application in clinical practices. To improve the accuracy of object boundary location, a Local-global module is proposed, which uses attention mechanism to model intra-sample and inter-sample long-range dependencies from local and global perspectives respectively. Meanwhile, we exploit a convolutional neural network with a generalization enhancement module which is able to adaptively adjust weights of the classifier during test to improve the generalization ability of the model. Furthermore, we design a multi-granularity loss function which can guide the network to supervise regions and boundary details from pixel-level, patch-level, and map-level, respectively. To verify the effectiveness of our proposed method, we conduct extensive experiments on five medical image datasets of different modalities, including complex lung tumors, cell nuclei, brain tumors, skin lesions, and colon polyps. Our method achieves better performance on the Dice coefficient and Hausdorff distance. We further verify the generalization ability of the model on the colon polyp dataset (including unseen sub-datasets in the testing set). © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Perturbation Analysis of Error Bounds for Convex Functions on Banach Spaces

arXiv

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arXiv 2024年

作者： Wei, Zhou Théra, Michel Yao, Jen-Chih Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China XLIM UMR CNRS 7252 Université de Limoges Limoges France Research Center for Interneural Computing China Medical University Hospital China Medical University Taichung Taiwan Academy of Romanian Scientists Bucharest50044 Romania

This paper focuses on the stability of both local and global error bounds for a proper lower semicontinuous convex function defined on a Banach space. Without relying on any dual space information, we first provide precise estimates of error bound moduli using directional derivatives. For a given proper lower semicontinuous convex function on a Banach space, we prove that the stability of local error bounds under small perturbations is equivalent to the directional derivative at a reference point having a non-zero minimum over the unit sphere. Additionally, the stability of global error bounds is shown to be equivalent to the infimum of the directional derivatives, at all points on the boundary of the solution set, being bounded away from zero over some neighborhood of the unit sphere. © 2024, CC0.

关键词： Banach spaces

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On Error Bounds of Inequalities in Asplund Spaces

arXiv

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arXiv 2023年

Error bounds are central objects in optimization theory and its applications. They were for a long time restricted only to the theory before becoming over the course of time a field of itself. This paper is devoted to the study of error bounds of a general inequality defined by a proper lower semicontinuous function on an Asplund space. Even though the results of the dual characterization on the error bounds of a general inequality (if one drops the convexity assumption) may not be valid, several necessary dual conditions are still obtained in terms of Fréchet/Mordukhovich subdifferentials of the concerned function at points in the solution set. Moreover, for an inequality defined by a composite-convex function that is to say by a function which is the composition of a convex function with a smooth mapping, such dual conditions also turn out to be sufficient to have the error bound property. Our work is an extension of results on dual characterizations of convex inequalities to the possibly non-convex case. © 2023, CC0.

关键词： Site selection

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Ct-Gan: A Conditional Generative Adversarial Network of Transformer Architecture for Text-to-Image

SSRN

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SSRN 2022年

作者： Wang, Bing Zhang, Xin Jiao, Wentao Tian, Xuedong College of Electronic Information Engineering Hebei University Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Hebei Baoding071000 China College of Mathematics and Information Science Hebei University Hebei Baoding071000 China College of Cyber Security and Computer Hebei University Hebei Baoding071000 China

How to generate an image from a text description is an imaginative and challenging task. This study proposes a conditional generative adversarial network (GAN) of transformer architecture for text-to-image tasks called CT-GAN by employing the GAN generator based on transformer architecture. We also propose a filtering module suitable for non-end-to-end multi-stage models. This module can screen out the good images generated in the previous stage and allows only the good images to participate in the generation of the later stage. This method significantly improves the quality of the generated images. Furthermore, we designed a generator and discriminator based on symmetry. In the generator, we propose a shift self-attention technology to establish information communication between grids, reduce boundary loss, and improve image quality. We established two modes of local and global discriminations based on the grid, which can balance the performance of the generator and discriminator, improve the training stability, and accelerate the model convergence. We conducted several experiments on the widely used conditional datasets (CUB and COCO) and unconditional datasets (CelebA and LSUN church). The experimental results show that the proposed CT-GAN is superior to the most advanced convolution model in generating diversity and semantic consistency. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Primal Characterizations of Stability of Error Bounds for Semi-infinite Convex Constraint Systems in Banach Spaces

arXiv

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arXiv 2023年

作者： Wei, Zhou Théra, Michel Yao, Jen-Chih Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China XLIM UMR CNRS 7252 Université de Limoges Limoges France Federation University Australia Ballarat Australia Research Center for Interneural Computing China Medical University Hospital China Medical University Taichung Taiwan

This article is devoted to the stability of error bounds (local and global) for semi-infinite convex constraint systems in Banach spaces. We provide primal characterizations of the stability of local and global error bounds when systems are subject to small perturbations. These characterizations are given in terms of the directional derivatives of the functions that enter into the definition of these systems. It is proved that the stability of error bounds is essentially equivalent to verifying that the optimal values of several minimax problems, defined in terms of the directional derivatives of the functions defining these systems, are outside of some neighborhood of zero. Moreover, such stability only requires that all component functions entering the system have the same linear perturbation. When these stability results are applied to the sensitivity analysis of Hoffman’s constants for semi-infinite linear systems, primal criteria for Hoffman’s constants to be uniformly bounded under perturbations of the problem data are *** Codes 90C31, 90C25, 49J52, 46B20 © 2023, CC0.

关键词： Sensitivity analysis

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Subtransversality and Strong CHIP of Closed Sets in Asplund Spaces

arXiv

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arXiv 2023年

In this paper, we mainly study subtransversality and two types of strong CHIP (given via Fréchet and limiting normal cones) for a collection of finitely many closed sets. We first prove characterizations of Asplund spaces in terms of subtransversality and intersection formulae of Fréchet normal cones. Several necessary conditions for subtransversality of closed sets are obtained via Fréchet/limiting normal cones in Asplund spaces. Then, we consider subtransversality for some special closed sets in convex-composite optimization. In this frame we prove an equivalence result on subtransversality, strong Fréchet CHIP and property (G) so as to extend a duality characterization of subtransversality of finitely many closed convex sets via strong CHIP and property (G) to the possibly non-convex case. As applications, we use these results on subtransversality and strong CHIP to study error bounds of inequality systems and give several dual criteria for error bounds via Fréchet normal cones and subdifferentials. © 2023, CC0.

关键词： Convex optimization

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DPA-2:a large atomic model as a multitask learner

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npj computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Primal Characterizations of Error Bounds for Composite-convex Inequalities

arXiv

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arXiv 2022年

作者： Wei, Zhou Théra, Michel Yao, Jen-Chih Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Xlim UMR-CNRS 7252 Université de Limoges Limoges France Federation University Australia Ballarat Australia Department of Applied Mathematics National Sun Yat-sen University Kaohsiung Taiwan

This paper is devoted to primal conditions of error bounds for a general function. In terms of Bouligand tangent cones, lower Hadamard directional derivatives and the Hausdorff-Pompeiu excess of subsets, we provide several necessary and/or sufficient conditions of error bounds with mild assumptions. Then we use these primal results to characterize error bounds for composite-convex functions (i.e. the composition of a convex function with a continuously differentiable mapping). It is proved that the primal characterization of error bounds can be established via Bouligand tangent cones, directional derivatives and the Hausdorff-Pompeiu excess if the mapping is metrically regular at the given point. The accurate estimate on the error bound modulus is also obtained. © 2022, CC0.

关键词： Mapping

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