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Moreover, combined with the specially designed Automatic Selection and Integration Module (ASIM), different stages of the recurrent model can elegantly implement self-ensemble learning and synergize the sub-networks to improve the overall performance. Extensive experiments demonstrate that our model achieves competitive results and strikes a good balance between the size, complexity, and performance of the model.<\/jats:p>","DOI":"10.1007\/s13042-024-02330-0","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T11:02:29Z","timestamp":1725447749000},"page":"1201-1218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A lightweight self-ensemble feedback recurrent network for fast MRI reconstruction"],"prefix":"10.1007","volume":"16","author":[{"given":"Juncheng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanhui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lok Ming","family":"Lui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tieyong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2330_CR1","doi-asserted-by":"crossref","unstructured":"Gao G, Li W, Li J, Wu F, Lu H, Yu Y (2022) Feature distillation interaction weighting network for lightweight image super-resolution. 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