{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T05:15:48Z","timestamp":1734585348566,"version":"3.30.2"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,15]]},"DOI":"10.1109\/embc53108.2024.10782322","type":"proceedings-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T19:07:21Z","timestamp":1734462441000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale Self-Supervised Consistency Training for Trustworthy Medical Imaging Classification"],"prefix":"10.1109","author":[{"given":"Bonian","family":"Han","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University,Department of Statistics,Hangzhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristian","family":"Moran","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University-San Antonio,Department of Computational, Engineering, and Mathematical Sciences,San Antonio,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeong","family":"Yang","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University-San Antonio,Department of Computational, Engineering, and Mathematical Sciences,San Antonio,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University-San Antonio,Department of Computational, Engineering, and Mathematical Sciences,San Antonio,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zechun","family":"Cao","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University-San Antonio,Department of Computational, Engineering, and Mathematical Sciences,San Antonio,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongbo","family":"Liang","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University-San Antonio,Department of Computational, Engineering, and Mathematical Sciences,San Antonio,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3331438"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8900286"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412498"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/mnras\/stac725"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006307"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12081948"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3270861"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2022.859973"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12020467"},{"article-title":"Dynamic feature alignment for semi-supervised domain adaptation","year":"2021","author":"Zhang","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3110805"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.832276"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956394"},{"key":"ref14","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"International conference on machine learning","author":"Guo"},{"article-title":"Regularizing neural networks by penalizing confident output distributions","year":"2017","author":"Pereyra","key":"ref15"},{"key":"ref16","first-page":"2810","article-title":"Trainable calibration measures for neural networks from kernel mean embeddings","volume-title":"Prco. ICML","author":"Kumar"},{"key":"ref17","first-page":"7933","article-title":"A consistent and differentiable lp canonical calibration error estimator","volume":"35","author":"Popordanoska","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000291"},{"article-title":"Improved trainable calibration method for neural networks on medical imaging classification","volume-title":"British Machine Vision Conference (BMVC)","author":"Liang","key":"ref19"},{"article-title":"Distilling the knowledge in a neural network","year":"2015","author":"Hinton","key":"ref20"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref22","first-page":"4694","article-title":"When does label smoothing help?","volume-title":"Proc. NeurIPS","author":"M\u00fcller"},{"article-title":"mixup: Beyond empirical risk minimization","volume-title":"Proc. ICLR","author":"Zhang","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.2172\/1525811"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.17632\/rscbjbr9sj.2"},{"key":"ref28","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke","year":"2019"},{"key":"ref29","first-page":"108","article-title":"API design for machine learning software: experiences from the scikit-learn project","volume-title":"ECML PKDD Workshop: Languages for Data Mining and Machine Learning","author":"Buitinck"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9602"},{"issue":"11","key":"ref31","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of machine learning research"}],"event":{"name":"2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","start":{"date-parts":[[2024,7,15]]},"location":"Orlando, FL, USA","end":{"date-parts":[[2024,7,19]]}},"container-title":["2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10781475\/10781494\/10782322.pdf?arnumber=10782322","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T07:12:40Z","timestamp":1734505960000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10782322\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,15]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/embc53108.2024.10782322","relation":{},"subject":[],"published":{"date-parts":[[2024,7,15]]}}}