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CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation

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Science China(Information Sciences) 2024年第5期67卷 43-55页

作者： Yunfan SHAO Zhichao GENG Yitao LIU Junqi DAI Hang YAN Fei YANG Zhe LI Hujun BAO Xipeng QIU School of computer Science Fudan University shanghai key laboratory of Intelligent Information processing Fudan University Zhejiang Lab

In this paper, we take the advantage of previous pre-trained models(PTMs) and propose a novel Chinese pre-trained unbalanced transformer(CPT). Different from previous Chinese PTMs, CPT is designed to utilize the shared knowledge between natural language understanding(NLU) and natural language generation(NLG) to boost the performance. CPT consists of three parts: a shared encoder, an understanding decoder, and a generation decoder. Two specific decoders with a shared encoder are pretrained with masked language modeling(MLM) and denoising auto-encoding(DAE) tasks, *** the partially shared architecture and multi-task pre-training, CPT can(1) learn specific knowledge of both NLU or NLG tasks with two decoders and(2) be fine-tuned flexibly that fully exploits the potential of the model. Moreover, the unbalanced transformer saves the computational and storage cost, which makes CPT competitive and greatly accelerates the inference of text generation. Experimental results on a wide range of Chinese NLU and NLG tasks show the effectiveness of CPT.

关键词： pre-trained model transformer language model generation unified model

Applications of artificial intelligence to lipid nanoparticle delivery

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Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of image processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

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Journal of Genetics and Genomics 2024年第4期51卷 443-453页

作者： Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China The key laboratory of medical Imaging Computing and computer assisted Intervention(MICCAI)of shanghai Shanghai 200032China shanghai key laboratory of Metabolic Remodeling and Health Institute of Metabolism and Integrative BiologyFudan UniversityShanghai 200433China Department of Ultrasound Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China Department of Breast surgery Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized *** present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic *** dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation *** better characterize tumor traits,12 multi-angle US images are fused using two distinct ***,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous *** radiomic and genomic features are screened using these *** addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC *** further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and *** together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

关键词： IMAGGS Radiogenomic framework "lmage-gene-geneset"associations Molecular subtypes Breast cancer

RepBoTNet-CESA:An Alzheimer’s Disease computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

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computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang College of computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China key laboratory of Intelligence image processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

A review of medical ocular image segmentation

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 181-202页

作者： Lai WEI Menghan HU shanghai key laboratory of Multidimensional Information processing East China Normal UniversityShanghai 200241China

Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in ***,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of *** article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular ***,the article introduces an overview of medical imaging,data processing,and performance evaluation ***,it analyzes recent developments in U-Net-based network ***,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.

关键词： medical image segmentation Orbit Tumor U-Net Transformer

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Simulation extractable SNARKs based on target linearly collision-resistant oracle

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Science China(Technological Sciences) 2024年第9期67卷 2853-2866页

作者： WANG LiGuan LI Yuan ZHANG ShuangJun CAI DongLiang KAN HaiBin shanghai key laboratory of Intelligent Information processing School of Computer ScienceFudan UniversityShanghai 200433China shanghai Engineering Research Center of Blockchain Shanghai 200433China Yiwu Research Institute of Fudan University Yiwu 322000China

The famous zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARK) was proposed by Groth in ***, the construction is based on quadratic arithmetic programs which are highly efficient concerning the proof length and the verification complexity. Since then, there has been much progress in designing zk-SNARKs, achieving stronger security,and simulated extractability, which is analogous to non-malleability and has broad applications. In this study, following Groth's pairing-based zk-SNARK, a simulation extractability zk-SNARK under the random oracle model is constructed. Our construction relies on a newly proposed property named target linearly collision-resistant, which is satisfied by random oracles under discrete logarithm assumptions. Compared to the original Groth16 zk-SNARK, in our construction, both parties are allowed to use such a random oracle, aiming to get the same random number. The resulting proof consists of 3 group elements and only 1 pairing equation needs to be verified. Compared to other related works, our construction is shorter in proof length and simpler in verification while preserving simulation extractability. The results also extend to achieve subversion zero-knowledge SNARKs.

关键词： quadratic arithmetic program simulation extractability subversion zero-knowledge succinct non-interactive arguments of knowledge target linearly collision-resistant

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GAN-DIRNet:A Novel Deformable image Registration Approach for Multimodal Histological images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of image processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of computer Science and Engineering North Minzu UniversityYinchuan750021China key laboratory of image and Graphics Intelligent processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE key laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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