{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:14:39Z","timestamp":1774620879769,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy","award":["K_G012002236201"],"award-info":[{"award-number":["K_G012002236201"]}]},{"name":"Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy","award":["K_G012002234001"],"award-info":[{"award-number":["K_G012002234001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce a novel teacher\u2013student network model that leverages the potency of our bespoke NoiseContextNet Block to discern and mitigate noise with unprecedented precision. This innovation is coupled with an iterative pruning technique aimed at refining the model for heightened computational efficiency without compromising the fidelity of denoising. We substantiate the superiority and effectiveness of our approach through a comprehensive suite of experiments, showcasing significant qualitative enhancements across a multitude of medical imaging modalities. The visual results from a vast array of tests firmly establish our method\u2019s dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising.<\/jats:p>","DOI":"10.3390\/s23239502","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:34:10Z","timestamp":1701304450000},"page":"9502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Enhancing Medical Image Denoising with Innovative Teacher\u2013Student Model-Based Approaches for Precision Diagnostics"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-4502","authenticated-orcid":false,"given":"Shakhnoza","family":"Muksimova","sequence":"first","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea"}]},{"given":"Sabina","family":"Umirzakova","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea"}]},{"given":"Sevara","family":"Mardieva","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102075","DOI":"10.1016\/j.inffus.2023.102075","article-title":"Medical Image Super-Resolution for Smart Healthcare Applications: A Comprehensive Survey","volume":"103","author":"Umirzakova","year":"2023","journal-title":"Inf. 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