{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:48:20Z","timestamp":1782366500706,"version":"3.54.5"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Adenocarcinoma\n                      <jats:italic>in situ<\/jats:italic>\n                      (AIS) and minimally invasive adenocarcinoma (MIA) typically present as ground-glass nodules (GGNs) on pulmonary computed tomography (CT), and their low contrast, blurred boundaries, and morphological variability pose substantial challenges to automated detection.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using YOLO12n as the baseline, we propose FAD-YOLO (Feature-refinement, Alignment, and Dynamic-sampling YOLO), a lightweight yet accurate detection framework with three task-oriented improvements: an A2C2f-FRFN module that replaces the standard feed-forward network within the Area-Attention C2f module with a Feature Refinement Feed-forward Network; a DySample dynamic upsampling module that replaces nearest-neighbor interpolation with content-aware learnable offsets; and a TADDH task-aligned dynamic detection head that combines a shared group-normalized convolution, deformable sampling in the localization branch, and layer-attention reweighting to alleviate classification\u2013localization misalignment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On an independent internal test set of 317 images, FAD-YOLO achieved a precision of 93.8%, a recall of 93.4%, an mAP@50 of 93.6%, and an mAP@50\u201395 of 70.8%, while reducing parameters by 18.7% relative to the baseline at the cost of a 12.5% increase in GFLOPs (from 6.4 to 7.2). Under identical settings, it performed comparably to or better than YOLOv5n, YOLOv8n, YOLO11n, and RT-DETR-R18, and outperformed the much larger RT-DETR-R50 on all accuracy metrics despite using approximately one-twentieth of its parameters. On an external Mendeley Data test set, the model achieved mAP@50 = 91.7% and mAP@50\u201395 = 68.2% without any fine-tuning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>FAD-YOLO achieves a favorable balance among accuracy, lightweight design, and cross-dataset generalization, and may serve as a candidate for further prospective validation as an aid to radiologists in AIS\/MIA discrimination on resource-constrained clinical devices.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1881220","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:30:03Z","timestamp":1782365403000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS\u2013MIA discrimination on pulmonary CT"],"prefix":"10.3389","volume":"13","author":[{"given":"Jinghui","family":"Chen","sequence":"first","affiliation":[{"name":"The First Clinical Medical College, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[{"name":"The First Clinical Medical College, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Sun","sequence":"additional","affiliation":[{"name":"The First Clinical Medical College, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengbin","family":"Ye","sequence":"additional","affiliation":[{"name":"The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianxin","family":"Xie","sequence":"additional","affiliation":[{"name":"The First Clinical Medical College, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjia","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, Fujian, 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