DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling
This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of lar...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925002836 |
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| author | Zongren Li Shuping Luo Hongwei Li Yanbin Li |
| author_facet | Zongren Li Shuping Luo Hongwei Li Yanbin Li |
| author_sort | Zongren Li |
| collection | DOAJ |
| description | This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1 × 1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation. |
| format | Article |
| id | doaj-art-8c1f473e6e324a5ab1ef624b0119e30f |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-8c1f473e6e324a5ab1ef624b0119e30f2025-08-20T03:48:15ZengElsevierNeuroImage1095-95722025-07-0131512128010.1016/j.neuroimage.2025.121280DCSLK: Combined large kernel shared convolutional model with dynamic channel SamplingZongren Li0Shuping Luo1Hongwei Li2Yanbin Li3Doctoral Candidate, Information Security, Medical Image Segmentation, Xinjiang University, Urumqi, ChinaDepartment of Gastroenterology, Joint Logistics Support Force of the Chinese People's Liberation Army, Multi-Modal Medical Image Segmentation, No 940 Hospital, Lanzhou, China; Corresponding author.Information Department, Bio-information Security, Joint Logistics Support Force of the Chinese People's Liberation Army, No 940 Hospital, Lanzhou, ChinaSchool of Computer Science and Technology, Medical Image Segmentation, Xinjiang University, Urumqi, ChinaThis study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1 × 1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.http://www.sciencedirect.com/science/article/pii/S1053811925002836Large convolutional kernelsReceptive fieldParameter sharing mechanismChannel compressionDynamic channel sampling |
| spellingShingle | Zongren Li Shuping Luo Hongwei Li Yanbin Li DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling NeuroImage Large convolutional kernels Receptive field Parameter sharing mechanism Channel compression Dynamic channel sampling |
| title | DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling |
| title_full | DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling |
| title_fullStr | DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling |
| title_full_unstemmed | DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling |
| title_short | DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling |
| title_sort | dcslk combined large kernel shared convolutional model with dynamic channel sampling |
| topic | Large convolutional kernels Receptive field Parameter sharing mechanism Channel compression Dynamic channel sampling |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925002836 |
| work_keys_str_mv | AT zongrenli dcslkcombinedlargekernelsharedconvolutionalmodelwithdynamicchannelsampling AT shupingluo dcslkcombinedlargekernelsharedconvolutionalmodelwithdynamicchannelsampling AT hongweili dcslkcombinedlargekernelsharedconvolutionalmodelwithdynamicchannelsampling AT yanbinli dcslkcombinedlargekernelsharedconvolutionalmodelwithdynamicchannelsampling |