Segmentation is an important task in a wide range of computer vision
applications, including medical image analysis. Recent years have seen an
increase in the complexity of medical image segmentation approaches based on
sophisticated convolutional neural network architectures. This progress has led
to incremental enhancements in performance on widely recognised benchmark
datasets. However, most of the existing approaches are computationally
demanding, which limits their practical applicability. This paper presents an
expand-squeeze dual multiscale residual network (ESDMR-Net), which is a fully
convolutional network that is particularly well-suited for resource-constrained
computing hardware such as mobile devices. ESDMR-Net focuses on extracting
multiscale features, enabling the learning of contextual dependencies among
semantically distinct features. The ESDMR-Net architecture allows dual-stream
information flow within encoder-decoder pairs. The expansion operation
(depthwise separable convolution) makes all of the rich features with
multiscale information available to the squeeze operation (bottleneck layer),
which then extracts the necessary information for the segmentation task. The
Expand-Squeeze (ES) block helps the network pay more attention to
under-represented classes, which contributes to improved segmentation accuracy.
To enhance the flow of information across multiple resolutions or scales, we
integrated dual multiscale residual (DMR) blocks into the skip connection. This
integration enables the decoder to access features from various levels of
abstraction, ultimately resulting in more comprehensive feature
representations. We present experiments on seven datasets from five distinct
examples of applications. Our model achieved the best results despite having
significantly fewer trainable parameters, with a reduction of two or even three
orders of magnitude.

提出了一种适用于资源受限的计算硬件的全卷积网络 ESDMR-Net，它专注于提取多尺度特征，通过扩展和压缩操作实现对分割任务所需信息的学习，并在跳跃连接中集成了双多尺度残差块，以增强多分辨率或尺度之间的信息流动。该模型在七个数据集上进行实验证明，尽管可训练参数显著减少了两到三个数量级，但取得了最佳结果。