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Information Processing in Agriculture 2023年第2期10卷 149-163页

作者： Yang Zhang Jun Yue Aihuan Song Shixiang Jia Zhenbo Li School of Information and Electrical Engineering Ludong UniversityYantai 264025PR China Marine Biology Institute of Shandong Province Qingdao 266104PR China School of Information and Electrical Engineering China Agricultural UniversityBeijing 100193PR China

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish *** study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network(CNN).We first establish the shellfish image(SI)dataset with 68 species and 93574 images,and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid *** the shellfish recognition with unbalanced samples,a hybrid loss function,including regularization term and focus loss term,is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total *** experimental results show that the accuracy of shell-fish recognition of the proposed method is 93.95%,13.68%higher than the benchmark network(VGG16),and the accuracy of shellfish recognition is improved by 0.46%,17.41%,17.36%,4.46%,1.67%,and 1.03%respectively compared with AlexNet,GoogLeNet,ResNet50,SN_Net,MutualNet,and ResNeSt,which are used to verify the efficiency of the proposed method.

关键词： Shellfish recognition High similarity Unbalanced samples Convolutional Neural Network Filter pruning and repairing hybrid loss function

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DT-Net:Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation

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Computers, Materials & Continua 2023年第9期76卷 3393-3411页

作者： Wenran Jia Simin Ma Peng Geng Yan Sun School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuangChina School of Mathematics and Information Science Zhangjiakou UniversityZhangjiakouChina

Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many *** acquired fundus images generally have the following problems:uneven illumination,high noise,and complex *** makes vessel segmentation very *** methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the *** address the limitations of convolution by introducing the transformer into retinal vessel ***,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named ***,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder ***,the local prediction results are obtained by the side output *** accuracy of the vessel segmentation is improved by the hybrid loss *** results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1.

关键词： Retinal vessel segmentation deformable convolution multi-scale transformer hybrid loss function

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MIA-UNet:Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

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Computer Modeling in Engineering & Sciences 2021年第11期129卷 805-828页

作者： Linfang Yu Zhen Qin Yi Ding Zhiguang Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care *** fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial *** the same time,important security and privacy issues have *** data uploaded to the cloud platform involves more personal attributes,health status and medical application data of *** leaked,abused or improperly disclosed,personal information security will be ***,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health *** meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile *** this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal ***,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and ***,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and *** with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%

关键词： Retinal vessel segmentation security and privacy redesigned skip connection feature maps aggregation hybrid loss function

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Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

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Computer Systems Science & Engineering 2023年第12期47卷 2835-2850页

作者： Fufang Li Manlin Luo Ming Hu Guobin Wang Yan Chen School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective *** address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet *** model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,***-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input *** the model uses deep supervision and a hybrid loss function for faster *** the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.

关键词： Liver and tumor segmentation unet attention gate swin transformer deep supervision hybrid loss function

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