{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:02:17Z","timestamp":1762351337977,"version":"3.37.3"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20390"],"award-info":[{"award-number":["U21A20390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Development Project of Jilin Province of China","award":["YDZJ202101ZYTS128"],"award-info":[{"award-number":["YDZJ202101ZYTS128"]}]},{"name":"Fundamental Research Funds for the Central University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for nucleus subtyping. Current CNN-based methods do not explicitly encode contextual information within the input images and point annotations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose a novel framework with context to locate and classify nuclei in microscopy image data. Specifically, first we use state-of-the-art network architectures to extract multi-scale feature representations from multi-field-of-view, multi-resolution input images and then conduct feature aggregation on-the-fly with stacked convolutional operations. Then, two auxiliary tasks are added to the model to effectively utilize the contextual information. One for predicting the frequencies of nuclei, and the other for extracting the regional distribution information of the same kind of nuclei. The entire framework is trained in an end-to-end, pixel-to-pixel fashion. We evaluate our method on two histopathological image datasets with different tissue and stain preparations, and experimental results demonstrate that our method outperforms other recent state-of-the-art models in nucleus identification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of our method is freely available at https:\/\/github.com\/qjxjy123\/DonRabbit.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac167","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T12:33:48Z","timestamp":1647520428000},"page":"2892-2898","source":"Crossref","is-referenced-by-count":4,"title":["Context-aware learning for cancer cell nucleus recognition in pathology images"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8060-4725","authenticated-orcid":false,"given":"Tian","family":"Bai","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University , 130012 Changchun, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University , 130012 Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University , 130012 Changchun, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University , 130012 Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenting","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University , 130012 Changchun, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University , 130012 Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyu","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University , 130012 Changchun, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University , 130012 Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, China-Japan Union Hospital of Jilin University , 130033 Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"year":"2020","author":"Bai","key":"2023020109104268300_btac167-B1"},{"key":"2023020109104268300_btac167-B3","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/JBHI.2016.2526603","article-title":"Hep-2 cell image classification with deep convolutional neural networks","volume":"21","author":"Gao","year":"2017","journal-title":"IEEE J. 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