MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
This work addresses the need for efficient and accurate segmentation in medical imaging, offering a light-weight solution that could benefit clinical diagnostics and treatments, though it appears incremental as it builds on existing multi-kernel and light-weight architectures.
The authors tackled medical image segmentation by proposing MKIS-Net, a light-weight multi-kernel network that enhances segmentation performance with fewer parameters, achieving competitive or superior results compared to state-of-the-art methods and reducing parameters by over an order of magnitude in some cases.
Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries. In this paper, we propose a multi-kernel image segmentation net (MKIS-Net), which uses multiple kernels to create an efficient receptive field and enhance segmentation performance. As a result of its multi-kernel design, MKIS-Net is a light-weight architecture with a small number of trainable parameters. Moreover, these multi-kernel receptive fields also contribute to better segmentation results. We demonstrate the efficacy of MKIS-Net on several tasks including segmentation of retinal vessels, skin lesion segmentation, and chest X-ray segmentation. The performance of the proposed network is quite competitive, and often superior, in comparison to state-of-the-art methods. Moreover, in some cases MKIS-Net has more than an order of magnitude fewer trainable parameters than existing medical image segmentation alternatives and is at least four times smaller than other light-weight architectures.