IEEE Transactions on Big Data最新文献

{"title":"COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations","authors":"Qingsen Yan;Bo Wang;Dong Gong;Chuan Luo;Wei Zhao;Jianhu Shen;Jingyang Ai;Qinfeng Shi;Yanning Zhang;Shuo Jin;Liang Zhang;Zheng You","doi":"10.1109/TBDATA.2021.3056564","DOIUrl":"10.1109/TBDATA.2021.3056564","url":null,"abstract":"A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"7 1","pages":"13-24"},"PeriodicalIF":7.2,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBDATA.2021.3056564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9561782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit","authors":"Tingyi Wanyan;Akhil Vaid;Jessica K De Freitas;Sulaiman Somani;Riccardo Miotto;Girish N. Nadkarni;Ariful Azad;Ying Ding;Benjamin S. Glicksberg","doi":"10.1109/TBDATA.2020.3048644","DOIUrl":"10.1109/TBDATA.2020.3048644","url":null,"abstract":"Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"7 1","pages":"38-44"},"PeriodicalIF":7.2,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBDATA.2020.3048644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature","authors":"Yifan Peng;Yuxing Tang;Sungwon Lee;Yingying Zhu;Ronald M. Summers;Zhiyong Lu","doi":"10.1109/TBDATA.2020.3035935","DOIUrl":"10.1109/TBDATA.2020.3035935","url":null,"abstract":"The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature, including those that report findings on radiographs. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved deep-learning (DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and fine-tuned a baseline deep neural network to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We fine-tuned an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared 15 clinical symptoms and 20 clinical findings of COVID-19 versus those of influenza to demonstrate the disease differences in the scientific publications. Our database is unique, as the figures are retrieved along with relevant text with fine-grained descriptions, and it can be extended easily in the future. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at \u0000<uri>https://github.com/ncbi-nlp/COVID-19-CT-CXR</uri>\u0000.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"7 1","pages":"3-12"},"PeriodicalIF":7.2,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBDATA.2020.3035935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10682543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis","authors":"Wenlu Zhang;Rongjian Li;Tao Zeng;Qian Sun;Sudhir Kumar;Jieping Ye;Shuiwang Ji","doi":"10.1109/TBDATA.2016.2573280","DOIUrl":"10.1109/TBDATA.2016.2573280","url":null,"abstract":"A central theme in learning from image data is to develop appropriate representations for the specific task at hand. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in \u0000<italic>Drosophila</i>\u0000, texture features were particularly effective for determining the developmental stages from in situ hybridization images. Such image representation is however not suitable for controlled vocabulary term annotation. Here, we developed feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain. To account for the differences between the source and target domains, we proposed a partial transfer learning scheme in which only part of the source model is transferred. We employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images. Results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"6 2","pages":"322-333"},"PeriodicalIF":7.2,"publicationDate":"2016-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBDATA.2016.2573280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10859846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}