{"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}
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}
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}
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}
{"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}
{"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}
{"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}
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}
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}
{"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}