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This work tries to do abnormal region detection on field of view cervical cell images based on deep learning, which is a novel way to solve cervical cytological screening problem. Since some abnormal nuclei gather in groups, the proposed method chooses abnormal regions instead of abnormal nuclei as the detection targets in order to locate the abnormal regions for the further diagnosis of the pathologists. In this study, a novel abnormal region detection approach for cervical screening is proposed based on a size\u2010sensitive fully convolutional network (R\u2010FCN). Due to the regular feature distribution, a fewer\u2010layer convolutional neural backbone network is designed for more efficient feature extraction and less running time. In addition, a new measure named hit degree is defined to describe the degree how closely each detected region and the corresponding ground truth matches up. Experimental results show that an average precision of 93.2% is achieved for abnormal region detection in cervical smear images. The proposed method is promising for the development of computer\u2010aided systems in clinical cervical cytological screening.<\/jats:p>","DOI":"10.1049\/iet-ipr.2018.6032","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T21:41:58Z","timestamp":1544823718000},"page":"583-590","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Abnormal region detection in cervical smear images based on fully convolutional network"],"prefix":"10.1049","volume":"13","author":[{"given":"Jianwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology Guangdong People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junting","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology Guangdong People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianfu","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangzhou LBP Medicine Science &amp; Technology Co., Ltd. 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