MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation

• URL: http://arxiv.org/abs/2210.08168v1
• Date: Sat, 15 Oct 2022 02:46:28 GMT
• Title: MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
• Authors: Tariq M. Khan, Muhammad Arsalan, Antonio Robles-Kelly, Erik Meijering
• Abstract summary: We propose a multi- kernel image segmentation net (MKIS-Net) MKIS-Net is a light-weight architecture with a small number of trainable parameters. We demonstrate the efficacy of MKIS-Net on several tasks including segmentation of retinal vessels, skin lesion segmentation, and chest X-ray segmentation.
• Score: 7.587725015524997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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