Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics
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 n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/23/9502 |
| _version_ | 1827592059715846144 |
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| author | Shakhnoza Muksimova Sabina Umirzakova Sevara Mardieva Young-Im Cho |
| author_facet | Shakhnoza Muksimova Sabina Umirzakova Sevara Mardieva Young-Im Cho |
| author_sort | Shakhnoza Muksimova |
| collection | DOAJ |
| description | 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–student 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’s dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising. |
| first_indexed | 2024-03-09T01:43:23Z |
| format | Article |
| id | doaj.art-c67ff60bdb3c48b18bd4d43c34dc6335 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:43:23Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c67ff60bdb3c48b18bd4d43c34dc63352023-12-08T15:26:13ZengMDPI AGSensors1424-82202023-11-012323950210.3390/s23239502Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision DiagnosticsShakhnoza Muksimova0Sabina Umirzakova1Sevara Mardieva2Young-Im Cho3Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of KoreaThe 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–student 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’s dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising.https://www.mdpi.com/1424-8220/23/23/9502medical image denoisinglightweight modelteacher–student networkmodel speed optimization |
| spellingShingle | Shakhnoza Muksimova Sabina Umirzakova Sevara Mardieva Young-Im Cho Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics Sensors medical image denoising lightweight model teacher–student network model speed optimization |
| title | Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics |
| title_full | Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics |
| title_fullStr | Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics |
| title_full_unstemmed | Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics |
| title_short | Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics |
| title_sort | enhancing medical image denoising with innovative teacher student model based approaches for precision diagnostics |
| topic | medical image denoising lightweight model teacher–student network model speed optimization |
| url | https://www.mdpi.com/1424-8220/23/23/9502 |
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