{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:00:39Z","timestamp":1770235239756,"version":"3.49.0"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.<\/jats:p>","DOI":"10.3389\/fnbot.2022.1059739","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T05:37:17Z","timestamp":1669354637000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Variational deep embedding-based active learning for the diagnosis of pneumonia"],"prefix":"10.3389","volume":"16","author":[{"given":"Jian","family":"Huang","sequence":"first","affiliation":[]},{"given":"Wen","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jiarun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Pekka","family":"Kuosmanen","sequence":"additional","affiliation":[]},{"given":"Guanqun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chuan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Yu","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12759","article-title":"Review on COVID-19 diagnosis models based on machine learning and deep learning approaches","author":"Alyasseri","year":"2022","journal-title":"Expert Syst"},{"key":"B2","first-page":"9368","article-title":"\u201cThe power of ensembles for active learning in image classification,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Beluch","year":"2018"},{"key":"B3","first-page":"562","article-title":"\u201cSelecting influential examples: active learning with expected model output changes,\u201d","volume-title":"European Conference on Computer Vision","author":"Freytag","year":"2014"},{"key":"B4","first-page":"319","article-title":"\u201cUnsupervised multi-view nonlinear graph embedding,\u201d","author":"Huang","year":"2018","journal-title":"UAI"},{"key":"B5","first-page":"161","article-title":"\u201cCOVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach,\u201d","author":"Jadon","year":"2021","journal-title":"Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, Vol. 11601"},{"key":"B6","article-title":"\u201cVariational deep embedding: an unsupervised and generative approach to clustering,\u201d","author":"Jiang","year":"2016","journal-title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"},{"key":"B7","first-page":"8166","article-title":"\u201cTask-aware variational adversarial active learning,\u201d","author":"Kim","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B8","first-page":"715","article-title":"\u201cCost-sensitive active learning for intracranial hemorrhage detection,\u201d","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Kuo","year":"2018"},{"key":"B9","first-page":"859","article-title":"\u201cAdaptive active learning for image classification,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Li","year":"2013"},{"key":"B10","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1162\/neco.1992.4.4.590","article-title":"Information-based objective functions for active data selection","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput"},{"key":"B11","first-page":"580","article-title":"\u201cEfficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network,\u201d","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Mahapatra","year":"2018"},{"key":"B12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1146\/annurev.neuro.24.1.167","article-title":"An integrative theory of prefrontal cortex function","volume":"24","author":"Miller","year":"2001","journal-title":"Ann. 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