2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第10页

Single Generative Networks for Stain Normalization and Quality Enhancement of Histological Images in Digital Pathology 数字病理组织图像染色归一化和质量增强的单生成网络

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624221

Xintian Mao, Jiansheng Wang, X. Tao, Yan Wang, Qingli Li, Xiufeng Zhou, Yonghe Zhang

{"title":"Single Generative Networks for Stain Normalization and Quality Enhancement of Histological Images in Digital Pathology","authors":"Xintian Mao, Jiansheng Wang, X. Tao, Yan Wang, Qingli Li, Xiufeng Zhou, Yonghe Zhang","doi":"10.1109/CISP-BMEI53629.2021.9624221","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624221","url":null,"abstract":"Stain normalization of histopathology images is a promising technique commonly used in computer-aided diagnosis. This process eliminates the effects of staining intensity and color difference (batch effects) from various pathologic imaging systems. In this paper, we are focusing on stain normalization and visual quality enhancement. Although state-of-the-art methods, such as CycleGAN, perform well in image style transfer, they have been limiting by raw imaging quality. This paper propose a novel framework, single generative networks (SGNet), to train the staining model. We yield data pre-augmentation instantiated by clarity-brightness-saturation (CBS) adjustment, and introduce max pooling between the input and the intermediate features and positional normalization (PONO) to optimize network structure. The proposed approach is evaluated by using the placental pathological samples with villi, trophoblast cells and vascular area. Feature fusion results on placental sample demonstrate the proposed model outperforms existing methods, ESPCN, CycleGAN and SegCN-Net. Ablation studies also show the necessity of additional components. We test this network on low-quality images from different imaging systems. Experimental results preserve detailed structural information of tissues and show desirable performances on generalization ability of histological image, which increases the segmentation accuracy for digital pathology diagnosis. These findings have the potential for the establishment of histological staining criterion, massive pathological images with batch effects can be normalized with the aid of authoritative staining benchmark.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Texture Enhancement of X-ray Images of Ischemic Femoral Head Necrosis 缺血性股骨头坏死x线图像的纹理增强

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624381

Silin Liu

引用次数: 0

A Split Edge Computing Doable Network for Object Detection base on Depthwise Separable Convolution 一种基于深度可分卷积的分离边缘计算可行目标检测网络

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624324

Qingfeng Wen, Wei Guo, Longji Li, Boyu Fan, Zaifeng Shi

{"title":"A Split Edge Computing Doable Network for Object Detection base on Depthwise Separable Convolution","authors":"Qingfeng Wen, Wei Guo, Longji Li, Boyu Fan, Zaifeng Shi","doi":"10.1109/CISP-BMEI53629.2021.9624324","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624324","url":null,"abstract":"Due to the outstanding distributed data processing efficiency, edge computing has become a research hotspot in the field of object detection. Convolutional Neural Network (CNN) improves recognition performance of machine vision greatly, yet, which is difficult to deploy on edge computing device for its huge amount of data and calculation. Traditional deployment frameworks operate CNN completely on the cloud center or edge devices, while the cloud-only method leads to intolerable delay and bandwidth consumption, the edge-only causes the failure of edge devices when supporting massive computing tasks. In this paper, we propose an efficient edge computing framework and build a small target detection network: Split Edge Computing Doable Network (SECDN). In this framework, the feature extraction part is implemented on the edge device, and the parameters are compressed to avoid high calculation cost of edge devices. Raw data is preprocessed locally, and the results are sent to the cloud center for final processing. SECDN realizes the collaborative work of edge and cloud, and reduces pressure of edge or cloud. The experimental results show that the detection accuracy of SECDN has no obvious worse compared with the state of art network while requiring much lower data and computing effort.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"R-26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time, CNN-Based Assistive Device for Visually Impaired People 实时，基于cnn的视障人士辅助设备

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624387

Khaled Jouini, Mohamed Hédi Maâloul, O. Korbaa

{"title":"Real-Time, CNN-Based Assistive Device for Visually Impaired People","authors":"Khaled Jouini, Mohamed Hédi Maâloul, O. Korbaa","doi":"10.1109/CISP-BMEI53629.2021.9624387","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624387","url":null,"abstract":"Visual impairment limits people's ability to move about unaided and interact with the surrounding world. This paper aims to leverage recent advances in deep learning to assist visually impaired people in their daily challenges. The high accuracy of deep learning comes at the expense of high computational requirements for both the training and the inference phases. To meet the computational requirements of deep learning, a common approach is to move data from the assistive device to distant servers (i.e. cloud-based inference). Such data movement requires a fast and active network connection and raises latency, cost, and privacy issues. In contrast with most of exiting assistive devices, in our work we move the computation to where data resides and opt for an approach where inference is performed directly “on” device (i.e. on-device-based inference). Running state-of-the-art deep learning models for a real-time inference on devices with limited resources is a challenging problem that cannot be solved without trading accuracy for speed (no free lunch). In this paper we conduct an extensive experimental study of 12 state-of-the-art object detectors, to strike the best trade-off between speed and accuracy. Our experimental study shows that by choosing the right models, frameworks, and compression techniques, we can achieve decent inference speed with very low accuracy drop.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128526029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Behavior Recognition Based on Improved Faster RCNN 基于改进的更快RCNN的行为识别

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624427

Bing Du, Ji Zhao, Mingyuan Cao, Mingyang Li, Hailong Yu

{"title":"Behavior Recognition Based on Improved Faster RCNN","authors":"Bing Du, Ji Zhao, Mingyuan Cao, Mingyang Li, Hailong Yu","doi":"10.1109/CISP-BMEI53629.2021.9624427","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624427","url":null,"abstract":"We divide the recognition process into “object detection” and “behavior prediction”. Firstly, all objects in the image are detected, and then the detection results are used as the input of the behavior recognition part to predict the interaction actions between objects. In the process of feature extraction, we add extra parameters to the sampling point of each convolution kernel to give the characteristic of convolution kernel deformation, so that the network has better adaptability to complex scenes. In the detection of target, the attention mechanism is combined with ResNet network, and the network structure is changed from “post-activation” to “pre-activation”, which makes the suggestion box have certain screening ability and avoids the phenomenon of overfitting. In action prediction, the network takes the instance object in the feature map as the center, the interactive objects around which are detected according to the appearance characteristics and attention weight of the object, and the action scores between them are predicted. Finally, our network is trained on the enhanced COCO dataset. Compared to traditional methods. The proposed method can well detect the actions in the image, and the mAP reaches 67.2%, an increase of nearly 14 percentage points, which is of high experimental value.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130708239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new convolutional neural network and long short term memory combined model for stock index prediction 一种新的卷积神经网络与长短期记忆相结合的股票指数预测模型

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624337

Yuyang Lin, Qiyin Zhong, Qi Huang, Muyang Li, Fei Ma

引用次数: 1

Deformable medical image registration based on unsupervised generative adversarial network integrating dual attention mechanisms 基于双注意机制的无监督生成对抗网络的形变医学图像配准

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624229

Meng Li, Yuwen Wang, Fuchun Zhang, Guoqiang Li, Shunbo Hu, Liang Wu

{"title":"Deformable medical image registration based on unsupervised generative adversarial network integrating dual attention mechanisms","authors":"Meng Li, Yuwen Wang, Fuchun Zhang, Guoqiang Li, Shunbo Hu, Liang Wu","doi":"10.1109/CISP-BMEI53629.2021.9624229","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624229","url":null,"abstract":"Registration is a basic subject of medical image research, and has been a research hotspot for decades. In the process of optimizing each pair of images, the traditional registration requires a lot of calculation, which is very time-consuming for a large amount of data. In recent years, the existing deep learning network framework, especially the model based on U-Net structure, has not only improved the computing speed, but also greatly improved the registration performance. However, the feature loss occurs in the UpSampling process of this structure. Hence, We propose a generative adversarial network using a dual attention mechanisms without any supervised information. In UpSampling process of the registration network, the dual attention mechanism is introduced to improve feature recovery ability. The dual attention mechanism consists of channel attention mechanism and location attention mechanism. For the registration network, local crosscorrelation loss functions are proposed to improve image similarity. Experiments show that our method has achieved perfect registration effect, especially in the edge region.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"94 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Efficiency Imaging Algorithm for the Multiple receivers Data in Synthetic Aperture Sonar 合成孔径声呐多接收机数据的高效成像算法

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624220

Haoran Wu, Jinsong Tang

引用次数: 0

Transductive Learning for BI-RADS Knowledge Graph based on Knowledge Tensor Factorization 基于知识张量分解的BI-RADS知识图的换能化学习

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624217

Jianing Xi, Zhaoji Miao, Qinghua Huang

{"title":"Transductive Learning for BI-RADS Knowledge Graph based on Knowledge Tensor Factorization","authors":"Jianing Xi, Zhaoji Miao, Qinghua Huang","doi":"10.1109/CISP-BMEI53629.2021.9624217","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624217","url":null,"abstract":"The advantage of Knowledge Graph (KG) can greatly prompt the interpretability of the artificial intelligence diagnosis. For breast ultrasound, the KG can be built through BI-RADS semantic descriptions, and the diagnosis can be achieved by link reconstruction between patients and outcomes. However, the existing KG analysis methods consider only the linked neighbors of the entities and relations during embedding, but not the whole entities and relations in KG, which reduces the link reconstruction power for diagnosis in the case of only a small fraction of labeled patients. In this paper, we present a transductive learning based Knowledge Tensor Factorization (KTF) method, which can effectively represent the KG data through a core tensor of interactions among all entities and relations and their embedding vectors. KTF demonstrates distinct diagnosis performance even if there is only a small fraction of labeled patients. Through experiments of assessments, KTF shows distinct superior performance in diagnosis for KG data of BI-RADS with a small fraction of known outcomes of patients.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Dynamic Clustering Method of Hot Topics Based on User Interaction and Text Similarity 基于用户交互和文本相似度的热点话题动态聚类方法

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624388

Shan Liu, Xiaoqing Wu, Jianping Chai

引用次数: 1