Annual Conference on Medical Image Understanding and Analysis最新文献

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CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection CellCentroidFormer:结合自注意和卷积的细胞检测
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.00338
Royden Wagner, K. Rohr
{"title":"CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection","authors":"Royden Wagner, K. Rohr","doi":"10.48550/arXiv.2206.00338","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00338","url":null,"abstract":"Cell detection in microscopy images is important to study how cells move and interact with their environment. Most recent deep learning-based methods for cell detection use convolutional neural networks (CNNs). However, inspired by the success in other computer vision applications, vision transformers (ViTs) are also used for this purpose. We propose a novel hybrid CNN-ViT model for cell detection in microscopy images to exploit the advantages of both types of deep learning models. We employ an efficient CNN, that was pre-trained on the ImageNet dataset, to extract image features and utilize transfer learning to reduce the amount of required training data. Extracted image features are further processed by a combination of convolutional and transformer layers, so that the convolutional layers can focus on local information and the transformer layers on global information. Our centroid-based cell detection method represents cells as ellipses and is end-to-end trainable. Furthermore, we show that our proposed model can outperform fully convolutional one-stage detectors on four different 2D microscopy datasets. Code is available at: https://github.com/roydenwa/cell-centroid-former","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058985","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
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting 同时语义和实例分割用于冒号核识别和计数
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-03-01 DOI: 10.48550/arXiv.2203.00157
Lihao Liu, Chenyang Hong, Angelica I. Avilés-Rivero, C. Schonlieb
{"title":"Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting","authors":"Lihao Liu, Chenyang Hong, Angelica I. Avilés-Rivero, C. Schonlieb","doi":"10.48550/arXiv.2203.00157","DOIUrl":"https://doi.org/10.48550/arXiv.2203.00157","url":null,"abstract":"We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications. In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework. Our solution is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. We first train a semantic and instance segmentation model separately. Our framework uses as backbone HoverNet and Cascade Mask-RCNN models. We then ensemble the results with a custom Non-Maximum Suppression embedding (NMS). In our framework, the semantic model computes a class prediction for the cells whilst the instance model provides a refined segmentation. We demonstrate, through our experimental results, that our model outperforms the provided baselines by a large margin.","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574505","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
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation 溃疡性结肠炎严重程度估计的类距离加权交叉熵损失
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-02-09 DOI: 10.1007/978-3-031-12053-4_12
G. Polat, I. Ergenc, H. Kani, Y. Alahdab, O. Atug, A. Temi̇zel
{"title":"Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation","authors":"G. Polat, I. Ergenc, H. Kani, Y. Alahdab, O. Atug, A. Temi̇zel","doi":"10.1007/978-3-031-12053-4_12","DOIUrl":"https://doi.org/10.1007/978-3-031-12053-4_12","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127524953","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: 12
Self-supervision and Multi-task Learning: Challenges in Fine-Grained COVID-19 Multi-class Classification from Chest X-rays 自我监督与多任务学习:胸片细粒度COVID-19多类分类的挑战
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-01-16 DOI: 10.1007/978-3-031-12053-4_18
Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarette, Ibrahim Almakky, Mohammad Yaqub
{"title":"Self-supervision and Multi-task Learning: Challenges in Fine-Grained COVID-19 Multi-class Classification from Chest X-rays","authors":"Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarette, Ibrahim Almakky, Mohammad Yaqub","doi":"10.1007/978-3-031-12053-4_18","DOIUrl":"https://doi.org/10.1007/978-3-031-12053-4_18","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122283229","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: 5
Contrastive Pretraining for Echocardiography Segmentation with Limited Data 有限数据超声心动图分割的对比预训练
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-01-16 DOI: 10.1007/978-3-031-12053-4_50
M. Saeed, Rand Muhtaseb, Mohammad Yaqub
{"title":"Contrastive Pretraining for Echocardiography Segmentation with Limited Data","authors":"M. Saeed, Rand Muhtaseb, Mohammad Yaqub","doi":"10.1007/978-3-031-12053-4_50","DOIUrl":"https://doi.org/10.1007/978-3-031-12053-4_50","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125324964","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: 10
GPU-Net: Lightweight U-Net with more diverse features GPU-Net:具有更多样化功能的轻量级U-Net
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2022-01-07 DOI: 10.1007/978-3-031-12053-4_17
Heng Yu, Dian Fan, Weihu Song
{"title":"GPU-Net: Lightweight U-Net with more diverse features","authors":"Heng Yu, Dian Fan, Weihu Song","doi":"10.1007/978-3-031-12053-4_17","DOIUrl":"https://doi.org/10.1007/978-3-031-12053-4_17","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124965825","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
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings BRAIN2DEPTH:从脑电图记录中分类认知状态的轻量级CNN模型
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2021-06-12 DOI: 10.1007/978-3-030-80432-9_30
Pankaja Pandey, K. Miyapuram
{"title":"BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings","authors":"Pankaja Pandey, K. Miyapuram","doi":"10.1007/978-3-030-80432-9_30","DOIUrl":"https://doi.org/10.1007/978-3-030-80432-9_30","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117200395","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: 8
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images 基于深度学习的多参数磁共振图像前列腺癌检测的假阳性/阴性率控制
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2021-06-04 DOI: 10.1007/978-3-030-80432-9_5
Z. Min, F. Bianco, Qianye Yang, Rachael Rodell, Wen Yan, D. Barratt, Yipeng Hu
{"title":"Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images","authors":"Z. Min, F. Bianco, Qianye Yang, Rachael Rodell, Wen Yan, D. Barratt, Yipeng Hu","doi":"10.1007/978-3-030-80432-9_5","DOIUrl":"https://doi.org/10.1007/978-3-030-80432-9_5","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129759665","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: 1
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers 利用扩散图和机器学习分类器对神经退行性帕金森病多巴胺转运体SPECT图像进行分类
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2021-04-06 DOI: 10.1007/978-3-030-80432-9_29
J. Ding, Chi-Hsiang Chu, Mong-Na Lo Huang, Chien-Ching Hsu
{"title":"Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers","authors":"J. Ding, Chi-Hsiang Chu, Mong-Na Lo Huang, Chien-Ching Hsu","doi":"10.1007/978-3-030-80432-9_29","DOIUrl":"https://doi.org/10.1007/978-3-030-80432-9_29","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123227848","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: 1
Reducing Textural Bias Improves Robustness of Deep Segmentation Models 减少纹理偏差提高深度分割模型的鲁棒性
Annual Conference on Medical Image Understanding and Analysis Pub Date : 2020-11-30 DOI: 10.1007/978-3-030-80432-9_23
Seoin Chai, D. Rueckert, Ahmed E. Fetit
{"title":"Reducing Textural Bias Improves Robustness of Deep Segmentation Models","authors":"Seoin Chai, D. Rueckert, Ahmed E. Fetit","doi":"10.1007/978-3-030-80432-9_23","DOIUrl":"https://doi.org/10.1007/978-3-030-80432-9_23","url":null,"abstract":"","PeriodicalId":274139,"journal":{"name":"Annual Conference on Medical Image Understanding and Analysis","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941889","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: 1
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