{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:21:34Z","timestamp":1780410094738,"version":"3.54.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012639","name":"Prince Sultan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012639","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.ins.2026.123669","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T15:06:09Z","timestamp":1779548769000},"page":"123669","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Federated few-shot learning with explainable prototype representations for tuberculosis detection in chest X-rays"],"prefix":"10.1016","volume":"754","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0796-3507","authenticated-orcid":false,"given":"Safa","family":"Ben Atitallah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8236-8746","authenticated-orcid":false,"given":"Maha","family":"Driss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-7423","authenticated-orcid":false,"given":"Anis","family":"Koubaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ins.2026.123669_bib0005","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s12913-024-12155-w","article-title":"Assessment of public literacy in tb prevention and control in the national 13th five-year plan for tuberculosis prevention and control (2016\u20132020) in china","volume":"25","author":"Ni","year":"2025","journal-title":"BMC Health Serv. Res."},{"key":"10.1016\/j.ins.2026.123669_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102125","article-title":"Deep learning for chest x-ray analysis: A survey","volume":"72","author":"\u00c7all\u0131","year":"2021","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.ins.2026.123669_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501296","article-title":"Federated learning for smart healthcare: A survey","volume":"55","author":"Nguyen","year":"2022","journal-title":"ACM Computing Surveys (Csur)"},{"key":"10.1016\/j.ins.2026.123669_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102949","article-title":"A systematic review of few-shot learning in medical imaging","author":"Pachetti","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ins.2026.123669_bib0030","first-page":"30","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123669_bib0035","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2374","article-title":"Federated few-shot learning","author":"Wang","year":"2023"},{"issue":"2","key":"10.1016\/j.ins.2026.123669_bib0040","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.imed.2023.06.001","article-title":"Deep learning models for tuberculosis detection and infected region visualization in chest x-ray images","volume":"4","author":"Sharma","year":"2024","journal-title":"Intell. Med."},{"key":"10.1016\/j.ins.2026.123669_bib0045","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","article-title":"Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123669_bib0050","doi-asserted-by":"crossref","first-page":"97417","DOI":"10.1109\/ACCESS.2024.3428446","article-title":"Tuberculosis detection from chest x-ray image modalities based on transformer and convolutional neural network","volume":"12","author":"Kotei","year":"2024","journal-title":"IEEE Access"},{"issue":"15","key":"10.1016\/j.ins.2026.123669_bib0055","doi-asserted-by":"crossref","first-page":"2562","DOI":"10.3390\/diagnostics13152562","article-title":"Joint diagnosis of pneumonia, covid-19, and tuberculosis from chest x-ray images: A deep learning approach","volume":"13","author":"Ahmed","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.ins.2026.123669_bib0060","doi-asserted-by":"crossref","first-page":"42839","DOI":"10.1109\/ACCESS.2023.3270774","article-title":"An improved densenet deep neural network model for tuberculosis detection using chest x-ray images","volume":"11","author":"Huy","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.ins.2026.123669_bib0065","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s00521-022-07258-6","article-title":"Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence","volume":"36","author":"Nafisah","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.ins.2026.123669_bib0070","article-title":"Optimized tuberculosis classification system for chest x-ray images: Fusing hyperparameter tuning with transfer learning approaches","author":"Wajgi","year":"2024","journal-title":"Eng. Rep."},{"key":"10.1016\/j.ins.2026.123669_bib0075","series-title":"Medical imaging 2021: Imaging informatics for healthcare, research, and applications","first-page":"161","article-title":"Covid-19 detection from scarce chest x-ray image data using few-shot deep learning approach","volume":"vol. 11601","author":"Jadon","year":"2021"},{"issue":"107826","key":"10.1016\/j.ins.2026.123669_bib0080","article-title":"Momentum contrastive learning for few-shot covid-19 diagnosis from chest ct images","volume":"113","author":"Chen","year":"2021","journal-title":"Pattern Recognit."},{"issue":"101911","key":"10.1016\/j.ins.2026.123669_bib0085","article-title":"Discriminative ensemble learning for few-shot chest x-ray diagnosis","volume":"68","author":"Paul","year":"2021","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.ins.2026.123669_bib0090","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-60861-6","article-title":"A multistage framework for respiratory disease detection and assessing severity in chest x-ray images","volume":"14","author":"Sahoo","year":"2024","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.ins.2026.123669_bib0095","doi-asserted-by":"crossref","first-page":"6451","DOI":"10.1109\/JBHI.2024.3473541","article-title":"Enhancing early Alzheimer\u2019s disease detection through big data and ensemble few-shot learning","volume":"29","author":"Ben Atitallah","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"10.1016\/j.ins.2026.123669_bib0100","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1109\/JBHI.2021.3053568","article-title":"Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of alzheimer\u2019s disease","volume":"25","author":"Song","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.ins.2026.123669_bib0105","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-75253-z","article-title":"Early screening of miliary tuberculosis with tuberculous meningitis based on few-shot learning with multiple windows and feature granularities","volume":"14","author":"Tian","year":"2024","journal-title":"Sci. Rep."},{"issue":"23","key":"10.1016\/j.ins.2026.123669_bib0110","doi-asserted-by":"crossref","first-page":"63017","DOI":"10.1007\/s11042-023-18065-z","article-title":"Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays","volume":"83","author":"Malik","year":"2024","journal-title":"Multimed. Tools Appl."},{"issue":"2","key":"10.1016\/j.ins.2026.123669_bib0115","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3390\/s23020743","article-title":"Dmfl_net: A federated learning-based framework for the classification of covid-19 from multiple chest diseases using x-rays","volume":"23","author":"Malik","year":"2023","journal-title":"Sensors"},{"issue":"100204","key":"10.1016\/j.ins.2026.123669_bib0120","article-title":"A federated learning framework for pneumonia image detection using distributed data","volume":"4","author":"Kareem","year":"2023","journal-title":"Healthc. Anal."},{"issue":"29","key":"10.1016\/j.ins.2026.123669_bib0125","doi-asserted-by":"crossref","first-page":"73273","DOI":"10.1007\/s11042-023-17194-9","article-title":"Hpfl: hyper-network guided personalized federated learning for multi-center tuberculosis chest x-ray diagnosis","volume":"83","author":"Liu","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.ins.2026.123669_bib0130","series-title":"Machine learning algorithms for industrial applications","first-page":"107","article-title":"Few shot learning for medical imaging","author":"Kotia","year":"2020"},{"issue":"8","key":"10.1016\/j.ins.2026.123669_bib0135","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/s25082369","article-title":"Neuronasal: Advanced ai-driven self-supervised learning approach for enhanced sinonasal pathology detection","volume":"25","author":"Atitallah","year":"2025","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.ins.2026.123669_bib0140","doi-asserted-by":"crossref","first-page":"3883","DOI":"10.32604\/cmc.2023.037413","article-title":"Covid-19 classification from x-ray images: An approach to implement federated learning on decentralized dataset","volume":"75","author":"Siddique","year":"2023","journal-title":"Computers, Materials, & Continua"},{"key":"10.1016\/j.ins.2026.123669_bib0145","author":"Fan"},{"issue":"9","key":"10.1016\/j.ins.2026.123669_bib0150","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.3390\/diagnostics13091532","article-title":"Federated learning for medical image analysis with deep neural networks","volume":"13","author":"Nazir","year":"2023","journal-title":"Diagnostics"},{"issue":"2","key":"10.1016\/j.ins.2026.123669_bib0155","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TNNLS.2022.3190359","article-title":"Personalized federated few-shot learning","volume":"35","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.ins.2026.123669_bib0160","series-title":"Artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.ins.2026.123669_bib0165","series-title":"Advances in neural information processing systems","first-page":"32","article-title":"Deep leakage from gradients","author":"Zhu","year":"2019"},{"key":"10.1016\/j.ins.2026.123669_bib0170","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"618","article-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.ins.2026.123669_bib0175","author":"Rahman"},{"key":"10.1016\/j.ins.2026.123669_bib0180","article-title":"Dataset of tuberculosis chest x-rays images","author":"Kiran","year":"2024","journal-title":"Mendeley Data"},{"key":"10.1016\/j.ins.2026.123669_bib0185","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"8432","article-title":"Fedproto: Federated prototype learning across heterogeneous clients","volume":"vol. 36","author":"Tan","year":"2022"},{"issue":"1","key":"10.1016\/j.ins.2026.123669_bib0190","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-97803-9","article-title":"Tuberculosis detection using few shot learning","volume":"15","author":"Riasat","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ins.2026.123669_bib0195","series-title":"2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)","first-page":"1","article-title":"Lightweight federated learning for covid-19, pneumonia, and tb from chest x-ray images","volume":"vol. 1","author":"Trivedi","year":"2023"},{"issue":"109874","key":"10.1016\/j.ins.2026.123669_bib0200","article-title":"Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review","volume":"188","author":"Atitallah","year":"2025","journal-title":"Comput. Biol. Med."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006006?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526006006?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T13:38:06Z","timestamp":1780407486000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526006006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":39,"alternative-id":["S0020025526006006"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123669","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Federated few-shot learning with explainable prototype representations for tuberculosis detection in chest X-rays","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123669","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123669"}}