Medical image segmentation is vital to the area of medical imaging because it
enables professionals to more accurately examine and understand the information
offered by different imaging modalities. The technique of splitting a medical
image into various segments or regions of interest is known as medical image
segmentation. The segmented images that are produced can be used for many
different things, including diagnosis, surgery planning, and therapy
evaluation.
In initial phase of research, major focus has been given to review existing
deep-learning approaches, including researches like MultiResUNet, Attention
U-Net, classical U-Net, and other variants. The attention feature vectors or
maps dynamically add important weights to critical information, and most of
these variants use these to increase accuracy, but the network parameter
requirements are somewhat more stringent. They face certain problems such as
overfitting, as their number of trainable parameters is very high, and so is
their inference time.
Therefore, the aim of this research is to reduce the network parameter
requirements using depthwise separable convolutions, while maintaining
performance over some medical image segmentation tasks such as skin lesion
segmentation using attention system and residual connections.

这篇研究论文的主要内容是关于医学图像分割，重点介绍了深度学习方法，包括 MultiResUNet 和 Attention U-Net 等变种，并探讨了使用深度分离卷积来减少网络参数需求的可行性。

改进医学图像分割中的 U-Net 变体的混合方法

A hybrid approach for improving U-Net variants in medical image  segmentation

In recent years Deep Learning has brought about a breakthrough in Medical
Image Segmentation. U-Net is the most prominent deep network in this regard,
which has been the most popular architecture in the medical imaging community.
Despite outstanding overall performance in segmenting multimodal medical
images, from extensive experimentations on challenging datasets, we found out
that the classical U-Net architecture seems to be lacking in certain aspects.
Therefore, we propose some modifications to improve upon the already
state-of-the-art U-Net model. Hence, following the modifications we develop a
novel architecture MultiResUNet as the potential successor to the successful
U-Net architecture. We have compared our proposed architecture MultiResUNet
with the classical U-Net on a vast repertoire of multimodal medical images.
Albeit slight improvements in the cases of ideal images, a remarkable gain in
performance has been attained for challenging images. We have evaluated our
model on five different datasets, each with their own unique challenges, and
have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%,
1.41%, and 0.62% respectively.

本论文提出了一种改进 U-Net 模型的多分辨率结构 MultiResUNet，相较于理想情况下的图像，在处理挑战性医学图像方面实现了显著的性能提高，此提升在五种不同的医学图像数据集中分别达到了 10.15％，5.07％，2.63％，1.41％和 0.62％的相对改进。