{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T13:32:07Z","timestamp":1793885527412,"version":"4.8.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A6A1A03039493"],"award-info":[{"award-number":["2021R1A6A1A03039493"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2120251015517"],"award-info":[{"award-number":["2120251015517"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Chest X-ray (CXR)-based recognition of pulmonary diseases remains challenging due to overlapping radiographic patterns, class imbalance, and variability across imaging sources. These factors often lead to unstable performance in large-scale multi-class classification tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this study, pulmonary disease recognition is formulated as a 21-class primary-label classification benchmark using an integrated multi-source dataset constructed from five public chest X-ray repositories. Although some source datasets originally contain multi-label annotations, the final benchmark is reorganized into a primary-label format, where each image is assigned one target label from the unified 21-class label space. This formulation is used as a controlled benchmark simplification rather than a complete representation of real-world multi-label clinical diagnosis. We propose a structured QMIX-ViT multi-expert framework, where disease categories are decomposed into specialized groups modeled by individual Vision Transformer (ViT) experts. The expert outputs are fused through a QMIX-inspired monotonic mixing mechanism to support consistent global decision-making. The proposed model is evaluated against convolutional and Transformer-based baselines, including ResNet, DenseNet, CheXNet, EfficientNet, ViT-Tiny, and ViT-Base, using Precision, Recall,\n                      <jats:italic>F<\/jats:italic>\n                      1-score, AUROC, and AUPRC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Experimental results show that the proposed framework achieves improved decision-level performance, particularly in Precision, Recall, and\n                      <jats:italic>F<\/jats:italic>\n                      1-score, under the constructed benchmark setting.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results suggest that disease-group expert decomposition and monotonic fusion can reduce inter-class interference and improve decision-level stability. Overall, the proposed QMIX-ViT framework provides a structured approach for multi-class chest X-ray classification under heterogeneous data conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1847086","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:16:28Z","timestamp":1786976188000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification"],"prefix":"10.3389","volume":"13","author":[{"given":"Teer","family":"Ba","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ChengLong","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghua","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Zeeshan","family":"Jhandir","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiz","family":"Muhammad Raza ur Rehman","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyu Sang","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University","place":["Gyeongsan, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"100332","DOI":"10.1016\/j.health.2024.100332","article-title":"A vision transformer 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