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This study aimed to develop a transformer deep learning (DL) model for detecting pituitary microadenomas based on non-contrast multiparametric MRI and explore the explainability techniques to enhance transparency in convolutional neural network (CNN)-based classification. The primary research question addressed is how to improve the accuracy, generalization, and interpretability of CNNs for microadenomas detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Non-contrast multiparametric MRI sella area scans of 590 patients were retrospectively collected from three hospitals. The development and comparison of 2D_DL, 2.5D_DL, 2D_multichannel, and transformer models for classification. By incorporating Explainable AI (XAI), including Gradient-weighted Class Activation Mapping(Grad-CAM) and SHapley Additive exPlanations (SHAP), we improve model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The performance of the 2D_multichannel model, with an area under the curve (AUC) of 0.893, was better to that of the 2D_T1SAG_DL, 2D_T1COR_DL, 2D_T2COR_DL (AUC, 0.884, 0.779, and 0.846, respectively). The performance of the transformer model, with an area under the curve (AUC) of 0.985, was superior to that of the 2.5D_T1SAG_DL, 2.5D_T1COR_DL, 2.5D_T2COR_DL (AUC, 0.763, 0.863, and 0.835, respectively). The non-contrast MRI-based 2.5D_DL transformer model all shows outperforming performance in the internal and two external test sets (AUC, 0.874, 0.829, and 0.819, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Given its robust diagnostic performance and enhanced interpretability, this model demonstrates significant potential for clinical translation as a decision-support tool in the detection of pituitary microadenomas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-026-02391-3","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:12:37Z","timestamp":1777713157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-7950","authenticated-orcid":false,"given":"Siru","family":"Kang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxia","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1678-5688","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,2]]},"reference":[{"key":"2391_CR1","doi-asserted-by":"publisher","unstructured":"Pugazenthi S, Pari SS, Zhang Z, Silverstein J, Kim AH, Patel B. 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