{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:35:02Z","timestamp":1758080102954,"version":"3.44.0"},"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Start-Up Project of CUIT","award":["KYTZ202120"],"award-info":[{"award-number":["KYTZ202120"]}]},{"name":"Sichuan Science and Technology Program","award":["2023ZYD00011"],"award-info":[{"award-number":["2023ZYD00011"]}]},{"name":"Key Project of the Open Fund of the Sichuan National Applied Mathematics Center","award":["2024-KDJJ-02-001"],"award-info":[{"award-number":["2024-KDJJ-02-001"]}]},{"name":"Key Project of the Open Fund of the Sichuan National Applied Mathematics Center","award":["2025-KFJJ-02-001"],"award-info":[{"award-number":["2025-KFJJ-02-001"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but there are still challenges of inefficient data labeling and feature extraction. This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. Secondly, we introduce the MsHop module, which effectively captures the fine textures and patterns in ultrasound images through the integration of multi-scale and high-order information, thereby improving diagnostic accuracy. Additionally, we develop a dual-branch loss function that leverages data correlation and clustering features to enhance feature extraction and model stability. The experiments on a gallbladder ultrasound dataset have confirmed the effectiveness of our algorithm, achieving an accuracy of 0.884, a specificity of 0.932, and a sensitivity of 0.912\u2014outperforming existing methods. The results exhibit lower variance, indicating improved model stability. Furthermore, the findings demonstrate that using active learning, one can achieve comparable results to those from the full dataset with only 35% of the data, reducing annotation costs and increasing model learning efficiency. Further research will concentrate on refining the algorithm for wider clinical use and identifying additional features that may further improve diagnostic accuracy.<\/jats:p>","DOI":"10.1371\/journal.pone.0330781","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:25:33Z","timestamp":1758043533000},"page":"e0330781","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis"],"prefix":"10.1371","volume":"20","author":[{"given":"Jia","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4565-6693","authenticated-orcid":true,"given":"Yu-Qian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"issue":"1","key":"pone.0330781.ref001","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/s41572-022-00398-y","article-title":"Gallbladder cancer","volume":"8","author":"JC Roa","year":"2022","journal-title":"Nat Rev Dis Primers"},{"key":"pone.0330781.ref002","unstructured":"International Agency for Research on Cancer. Gallbladder Fact Sheet. 2022. https:\/\/gco.iarc.who.int\/media\/globocan\/factsheets\/cancers\/12-gallbladder-fact-sheet.pdf"},{"issue":"3","key":"pone.0330781.ref003","first-page":"209","article-title":"Global cancer statistics 2020 : GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"H Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"pone.0330781.ref004","doi-asserted-by":"crossref","unstructured":"Abou-Alfa GK, Jarnagin W, El Dika I, D\u2019Angelica M, Lowery M, Brown K. Liver and bile duct cancer. Abeloff\u2019s clinical oncology. Elsevier; 2020. p. 1314\u201341.","DOI":"10.1016\/B978-0-323-47674-4.00077-3"},{"issue":"9","key":"pone.0330781.ref005","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1158\/1055-9965.EPI-21-0265","article-title":"Incidence, mortality of cancers of the biliary tract, gallbladder,, liver by sex, age, race\/ethnicity and stage at diagnosis: United States 2013 to 2017","volume":"30","author":"TD Ellington","year":"2021","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"issue":"22","key":"pone.0330781.ref006","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.3748\/wjg.v26.i22.2967","article-title":"Benign gallbladder diseases: Imaging techniques and tips for differentiating with malignant gallbladder diseases","volume":"26","author":"MH Yu","year":"2020","journal-title":"World J Gastroenterol"},{"issue":"2","key":"pone.0330781.ref007","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S1499-3872(15)60351-4","article-title":"Contrast-enhanced ultrasound in diagnosis of gallbladder adenoma","volume":"14","author":"H-X Yuan","year":"2015","journal-title":"Hepatobiliary Pancreat Dis Int"},{"issue":"6","key":"pone.0330781.ref008","doi-asserted-by":"crossref","first-page":"3489","DOI":"10.3390\/app13063489","article-title":"Identifying malignant breast ultrasound images using ViT-patch","volume":"13","author":"H Feng","year":"2023","journal-title":"Applied Sciences"},{"key":"pone.0330781.ref009","doi-asserted-by":"crossref","first-page":"106629","DOI":"10.1016\/j.compbiomed.2023.106629","article-title":"HCTNet: a hybrid CNN-transformer network for breast ultrasound image segmentation","volume":"155","author":"Q He","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0330781.ref010","doi-asserted-by":"crossref","first-page":"105279","DOI":"10.1016\/j.ijmedinf.2023.105279","article-title":"Research related to the diagnosis of prostate cancer based on machine learning medical images: a review","volume":"181","author":"X Chen","year":"2024","journal-title":"Int J Med Inform"},{"key":"pone.0330781.ref011","doi-asserted-by":"crossref","first-page":"102326","DOI":"10.1016\/j.compmedimag.2024.102326","article-title":"MicroSegNet: a deep learning approach for prostate segmentation on micro-ultrasound images","volume":"112","author":"H Jiang","year":"2024","journal-title":"Comput Med Imaging Graph"},{"key":"pone.0330781.ref012","doi-asserted-by":"crossref","first-page":"106389","DOI":"10.1016\/j.compbiomed.2022.106389","article-title":"Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules","volume":"155","author":"H Gong","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0330781.ref013","doi-asserted-by":"crossref","first-page":"105430","DOI":"10.1016\/j.bspc.2023.105430","article-title":"Fully automated thyroid ultrasound screening utilizing multi-modality image and anatomical prior","volume":"87","author":"J Zhou","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"8","key":"pone.0330781.ref014","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1016\/j.ultrasmedbio.2023.03.022","article-title":"Establishment and evaluation of intelligent diagnostic model for ophthalmic ultrasound images based on deep learning","volume":"49","author":"Z Li","year":"2023","journal-title":"Ultrasound Med Biol"},{"issue":"2","key":"pone.0330781.ref015","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1038\/s41433-023-02705-7","article-title":"Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images","volume":"38","author":"L Feng","year":"2024","journal-title":"Eye (Lond)"},{"issue":"9","key":"pone.0330781.ref016","doi-asserted-by":"crossref","first-page":"4352","DOI":"10.1109\/JBHI.2023.3282596","article-title":"Deep learning for detection and localization of B-lines in lung ultrasound","volume":"27","author":"RT Lucassen","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"pone.0330781.ref017","doi-asserted-by":"crossref","first-page":"109926","DOI":"10.1016\/j.asoc.2022.109926","article-title":"Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees","volume":"133","author":"LL Custode","year":"2023","journal-title":"Appl Soft Comput"},{"key":"pone.0330781.ref018","doi-asserted-by":"crossref","first-page":"102629","DOI":"10.1016\/j.media.2022.102629","article-title":"A review on deep-learning algorithms for fetal ultrasound-image analysis","volume":"83","author":"MC Fiorentino","year":"2023","journal-title":"Med Image Anal"},{"issue":"2","key":"pone.0330781.ref019","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1002\/uog.26130","article-title":"Use of artificial intelligence and deep learning in fetal ultrasound imaging","volume":"62","author":"R Ramirez Zegarra","year":"2023","journal-title":"Ultrasound Obstet Gynecol"},{"issue":"9","key":"pone.0330781.ref020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3472291","article-title":"A survey of deep active learning","volume":"54","author":"P Ren","year":"2021","journal-title":"ACM Computing Surveys"},{"key":"pone.0330781.ref021","doi-asserted-by":"crossref","unstructured":"Dong Z, Huang X, Yuan G, Zhu H, Xiong H. Butterfly-core community search over labeled graphs. Proceedings of the VLDB Endowment. 2021;14(11):2006\u201318.","DOI":"10.14778\/3476249.3476258"},{"key":"pone.0330781.ref022","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1045","article-title":"Self-supervised learning methods and applications in medical imaging analysis: a survey","volume":"8","author":"S Shurrab","year":"2022","journal-title":"PeerJ Comput Sci"},{"key":"pone.0330781.ref023","doi-asserted-by":"crossref","unstructured":"Hang J, Dong Z, Zhao H, Song X, Wang P, Zhu H. Outside in: Market-aware heterogeneous graph neural network for employee turnover prediction. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. p. 353\u201362.","DOI":"10.1145\/3488560.3498483"},{"issue":"4","key":"pone.0330781.ref024","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1109\/TKDE.2022.3140866","article-title":"MANE: organizational network embedding with multiplex attentive neural networks","volume":"35","author":"Y Ye","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"pone.0330781.ref025","doi-asserted-by":"crossref","first-page":"102062","DOI":"10.1016\/j.media.2021.102062","article-title":"A survey on active learning and human-in-the-loop deep learning for medical image analysis","volume":"71","author":"S Budd","year":"2021","journal-title":"Med Image Anal"},{"key":"pone.0330781.ref026","first-page":"14681","article-title":"Topic modeling revisited: a document graph-based neural network perspective","volume":"34","author":"D Shen","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0330781.ref027","doi-asserted-by":"crossref","unstructured":"Nguyen NQ, Le TS. A semi-supervised learning method to remedy the lack of labeled data. In: 2021 15th International Conference on Advanced Computing and Applications (ACOMP). 2021. p. 78\u201384.","DOI":"10.1109\/ACOMP53746.2021.00017"},{"key":"pone.0330781.ref028","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"JB Grill","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"pone.0330781.ref029","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1002\/ima.22693","article-title":"CR-SSL: a closely related self-supervised learning based approach for improving breast ultrasound tumor segmentation","volume":"32","author":"AK Mishra","year":"2022","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"pone.0330781.ref030","doi-asserted-by":"crossref","unstructured":"Zhao Z, Yang G. Unsupervised contrastive learning of radiomics, deep features for label-efficient tumor classification. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II, 2021. p. 252\u201361.","DOI":"10.1007\/978-3-030-87196-3_24"},{"key":"pone.0330781.ref031","first-page":"1847","article-title":"Self-supervised representation learning for ultrasound video","volume":"2020","author":"J Jiao","year":"2020","journal-title":"Proc IEEE Int Symp Biomed Imaging"},{"key":"pone.0330781.ref032","doi-asserted-by":"crossref","unstructured":"Qi H, Collins S, Noble JA. Knowledge-guided pretext learning for utero-placental interface detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I. 2020. p. 582\u201393.","DOI":"10.1007\/978-3-030-59710-8_57"},{"issue":"11","key":"pone.0330781.ref033","doi-asserted-by":"crossref","first-page":"7199","DOI":"10.1002\/mp.15172","article-title":"TN-USMA Net: Triple normalization-based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound-specific pretraining and meta attention","volume":"48","author":"C Liu","year":"2021","journal-title":"Med Phys"},{"key":"pone.0330781.ref034","doi-asserted-by":"crossref","unstructured":"Basu S, Singla S, Gupta M, Rana P, Gupta P, Arora C. Unsupervised contrastive learning of image representations from ultrasound videos with hard negative mining. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022. p. 423\u201333.","DOI":"10.1007\/978-3-031-16440-8_41"},{"key":"pone.0330781.ref035","doi-asserted-by":"crossref","unstructured":"Basu S, Gupta M, Madan C, Gupta P, Arora C. FocusMAE: gallbladder cancer detection from ultrasound videos with focused masked autoencoders. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2024. p. 11715\u201325.","DOI":"10.1109\/CVPR52733.2024.01113"},{"issue":"4","key":"pone.0330781.ref036","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s11548-016-1515-z","article-title":"Automatic gallbladder and gallstone regions segmentation in ultrasound image","volume":"12","author":"J Lian","year":"2017","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"pone.0330781.ref037","doi-asserted-by":"crossref","first-page":"7700","DOI":"10.1038\/s41598-020-64205-y","article-title":"Deep learning-based decision support system for the diagnosis of neoplastic gallbladder polyps on ultrasonography: Preliminary results","volume":"10","author":"Y Jeong","year":"2020","journal-title":"Sci Rep"},{"issue":"16","key":"pone.0330781.ref038","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.3390\/jcm10163585","article-title":"Gallbladder polyp classification in ultrasound images using an ensemble convolutional neural network model","volume":"10","author":"T Kim","year":"2021","journal-title":"J Clin Med"},{"key":"pone.0330781.ref039","doi-asserted-by":"crossref","unstructured":"Basu S, Gupta M, Rana P, Gupta P, Arora C. Surpassing the human accuracy: detecting gallbladder cancer from USG images with curriculum learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022. p. 20886\u201396.","DOI":"10.1109\/CVPR52688.2022.02022"},{"key":"pone.0330781.ref040","doi-asserted-by":"crossref","unstructured":"Shuvo SB, Chowdhury MZ. Classification of gallbladder cancer using average ensemble learning. In: 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). 2024. p. 1450\u20135.","DOI":"10.1109\/ICEEICT62016.2024.10534480"},{"key":"pone.0330781.ref041","article-title":"Active learning for convolutional neural networks: a core-set approach","author":"O Sener","year":"2017","journal-title":"arXiv preprint"},{"key":"pone.0330781.ref042","article-title":"Bayesian batch active learning as sparse subset approximation","volume":"32","author":"R Pinsler","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0330781.ref043","doi-asserted-by":"crossref","unstructured":"Sinha S, Ebrahimi S, Darrell T. Variational adversarial active learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019. p. 5972\u201381.","DOI":"10.1109\/ICCV.2019.00607"},{"key":"pone.0330781.ref044","doi-asserted-by":"crossref","unstructured":"Xie B, Yuan L, Li S, Liu CH, Cheng X, Wang G. Active learning for domain adaptation: an energy-based approach. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022. p. 8708\u201316.","DOI":"10.1609\/aaai.v36i8.20850"},{"key":"pone.0330781.ref045","doi-asserted-by":"crossref","unstructured":"Cabannes V, Bottou L, Lecun Y, Balestriero R. Active self-supervised learning: a few low-cost relationships are all you need. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2023. p. 16274\u201383.","DOI":"10.1109\/ICCV51070.2023.01491"},{"key":"pone.0330781.ref046","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning. 2020. p. 1597\u2013607."},{"key":"pone.0330781.ref047","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020. p. 9729\u201338.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"pone.0330781.ref048","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"M Caron","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0330781.ref049","unstructured":"Zbontar J, Jing L, Misra I, LeCun Y, Deny S. Barlow twins: Self-supervised learning via redundancy reduction. In: International Conference On Machine Learning, 2021. p. 12310\u201320."},{"key":"pone.0330781.ref050","doi-asserted-by":"crossref","unstructured":"He K, Chen X, Xie S, Li Y, Dollar P, Girshick R. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2022. p. 16000\u20139.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"pone.0330781.ref051","doi-asserted-by":"crossref","unstructured":"Chen K, Liu Z, Hong L, Xu H, Li Z, Yeung DY. Mixed autoencoder for self-supervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2023. p. 22742\u201351.","DOI":"10.1109\/CVPR52729.2023.02178"},{"key":"pone.0330781.ref052","article-title":"Beit: bert pre-training of image transformers","author":"H Bao","year":"2021","journal-title":"arXiv preprint"},{"issue":"4","key":"pone.0330781.ref053","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1109\/TPAMI.2023.3336525","article-title":"Contrastive masked autoencoders are stronger vision learners","volume":"46","author":"Z Huang","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0330781.ref054","article-title":"A simple, efficient and scalable contrastive masked autoencoder for learning visual representations","author":"S Mishra","year":"2022","journal-title":"arXiv preprint"},{"key":"pone.0330781.ref055","article-title":"Dinov2: learning robust visual features without supervision","author":"M Oquab","year":"2023","journal-title":"arXiv preprint"},{"key":"pone.0330781.ref056","unstructured":"Higgins I, Matthey L, Pal A, Burgess C, Glorot X, Botvinick M. beta-VAE: learning basic visual concepts with a constrained variational framework. 2017. https:\/\/openreview.net\/forum?id=Sy2fzU9gl"},{"issue":"11","key":"pone.0330781.ref057","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"I Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"key":"pone.0330781.ref058","article-title":"Vicreg: variance-invariance-covariance regularization for self-supervised learning","author":"A Bardes","year":"2021","journal-title":"arXiv preprint"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0330781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:25:44Z","timestamp":1758043544000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0330781"}},"subtitle":[],"editor":[{"given":"Li","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":58,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0330781","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}