{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:17:07Z","timestamp":1787894227017,"version":"build-2784847793"},"reference-count":26,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB4704000"],"award-info":[{"award-number":["2023YFB4704000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB4704005"],"award-info":[{"award-number":["2023YFB4704005"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008963","name":"Guangdong University of Petrochemical Technology","doi-asserted-by":"crossref","award":["2024KJCX043"],"award-info":[{"award-number":["2024KJCX043"]}],"id":[{"id":"10.13039\/100008963","id-type":"DOI","asserted-by":"crossref"}]},{"award":["2024KJCX043"],"award-info":[{"award-number":["2024KJCX043"]}],"id":[{"id":"https:\/\/ror.org\/030ffke25","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Medical-image annotation is costly and limits the use of fully supervised learning. This study proposes the Self-Supervised Stacked Masked Denoising Autoencoder (S2MDAE) for four-class brain MRI classification. During pre-training, three convolutional encoder\u2013decoder blocks perform layer-wise masked reconstruction under a 75% mask ratio and Gaussian corruption; the pretrained encoder stack is then fine-tuned for classification. On the primary dataset, S2MDAE achieved 87.207% Accuracy, while ablation studies supported the contributions of pre-training, masking, noise injection, and layer-wise reconstruction. Representation analysis further showed increased class separability in deeper blocks after fine-tuning. Without retraining, external evaluation on a duplicate-screened independent dataset achieved 67.254% Accuracy and 66.009% Macro-F1, indicating partial transfer under domain shift. These findings support the task-specific value of the proposed framework while showing that broader clinical generalization requires further validation.<\/jats:p>","DOI":"10.3390\/s26175388","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:59:23Z","timestamp":1787741963000},"page":"5388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Supervised Stacked Masked Denoising Autoencoder (S2MDAE) for Brain MRI Denoising and Feature Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7548-9005","authenticated-orcid":false,"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5606-1817","authenticated-orcid":false,"given":"Puyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglin","family":"Wen","sequence":"additional","affiliation":[{"name":"Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoxi","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tomasila, G., and Emanuel, A.W.R. 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