{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:03:45Z","timestamp":1782482625838,"version":"3.54.5"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists\u2014particularly in underdeveloped countries\u2014alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images\u2014combining a Siamese network model with a triplet-based loss function\u2014to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework\u2019s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets.<\/jats:p>","DOI":"10.3390\/a18090567","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T11:51:12Z","timestamp":1757332272000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Few-Shot Breast Cancer Diagnosis Using a Siamese Neural Network Framework and Triplet-Based Loss"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1737-5680","authenticated-orcid":false,"given":"Tea","family":"Marasovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1873-6353","authenticated-orcid":false,"given":"Vladan","family":"Papi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"ref_1","first-page":"477","article-title":"Breast cancer statistics 2024","volume":"74","author":"Giaquinto","year":"2024","journal-title":"CA Cancer J. 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