{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:14:49Z","timestamp":1762809289428,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation for Young Scholars of China","award":["61803344"],"award-info":[{"award-number":["61803344"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this work, we propose a symmetry approach and design a convolutional neural network for mouse pose estimation under scale variation. The backbone adopts the UNet structure, uses the residual network to extract features, and adds the ASPP module into the appropriate residual units to expand the perceptual field, and uses the deep and shallow feature fusion to fuse and process the features at multiple scales to capture the various spatial relationships related to body parts to improve the recognition accuracy of the model. Finally, a set of prediction results based on heat map and coordinate offset is generated. We used our own built mouse dataset and obtained state-of-the-art results on the dataset.<\/jats:p>","DOI":"10.3390\/sym14071437","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"1437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detection of Key Points in Mice at Different Scales via Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhengyang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electrical Information, Zhengzhou University, Zhengzhou 450000, China"}]},{"given":"Ruiqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Henan Light Industry Vocational College, Zhengzhou 450000, China"}]},{"given":"Zhizhong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Information, Zhengzhou University, Zhengzhou 450000, China"}]},{"given":"Songwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Information, Zhengzhou University, Zhengzhou 450000, China"}]},{"given":"Juncai","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical Information, Zhengzhou University, Zhengzhou 450000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.neuron.2016.12.041","article-title":"Neuroscience Needs Behavior: Correcting a Reductionist Bias","volume":"93","author":"Krakauer","year":"2017","journal-title":"Neuron"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1111\/2041-210X.13166","article-title":"Tracktor: Image-based automated tracking of animal movement and behaviour","volume":"10","author":"Sridhar","year":"2018","journal-title":"Methods Ecol. 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