论文标题
MKIS-NET：用于医疗图像细分的轻量重量多内核网络
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
论文作者
论文摘要
图像分割是医学成像中的重要任务。它构成了多种临床诊断方法，治疗方法和计算机辅助手术的骨干。在本文中，我们提出了一个多内核图像分割网（MKIS-NET），该网络使用多个内核来创建一个有效的接受场并增强分割性能。由于其多内核设计，MKIS-NET是具有少量可训练参数的轻质体系结构。此外，这些多内核接受场也有助于更好的分割结果。我们证明了MKIS-NET对多个任务的功效，包括视网膜血管的分割，皮肤病变分割和胸部X射线分割。与最先进的方法相比，提议的网络的性能非常具竞争力，而且通常是优越的。此外，在某些情况下，MKIS-NET的训练参数比现有的医学图像分割替代方案要少于训练参数的数量级，并且至少是其他轻量级体系结构的四倍。
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.
