"DCSLK: Combined Large Kernel Shared Convolutional Model with Dynamic Channel Sampling".

in NeuroImage by Zongren Li, Shuping Luo, Hongwei Li, Yanbin Li

TLDR

  • The study proposes an innovative approach to enhance the performance of computer vision tasks by combining large and small convolutional kernels, showcasing promising results in brain tumor segmentation.
  • The proposed method surpasses current mainstream architectures across all performance metrics, offering potential applications in medical image segmentation.

Abstract

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.

Overview

  • The study focuses on exploring the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in computer vision.
  • The study highlights the issues of parameters and computational complexity arising from the use of large convolutional kernels and explores novel solutions to address these issues.
  • The study aims to enhance the performance of computer vision tasks via an innovative approach that combines the benefits of large convolutional kernels and small convolutional kernels.

Comparative Analysis & Findings

  • The study conducts a comprehensive evaluation on three brain tumor segmentation datasets and compares the proposed method with mainstream ConvNet and Transformer architectures.
  • The experimental results show that the proposed method surpasses current mainstream architectures across all performance metrics.
  • The study highlights the effectiveness of the proposed dynamic channel sampling approach in addressing spatial feature information loss and augmented memory access during channel compression.

Implications and Future Directions

  • The study provides novel research perspectives and technical strategies for medical image segmentation, offering potential applications in computer-aided diagnosis and treatment planning.
  • Future research directions could explore the adaptation of the proposed method to other computer vision tasks and datasets.
  • The study`s findings highlight the need for further exploration of models that effectively leverage large convolutional kernels while addressing the issues of parameters and computational complexity.
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