In this paper, we present a high-performance deep neural network for weak
target image segmentation, including medical image segmentation and infrared
image segmentation. To this end, this work analyzes the existing dynamic
convolutions and proposes dynamic parameter convolution (DPConv). Furthermore,
it reevaluates the relationship between reconstruction tasks and segmentation
tasks from the perspective of DPConv, leading to the proposal of a dual-network
model called the Siamese Reconstruction-Segmentation Network (SRSNet). The
proposed model is not only a universal network but also enhances the
segmentation performance without altering its structure, leveraging the
reconstruction task. Additionally, as the amount of training data for the
reconstruction network increases, the performance of the segmentation network
also improves synchronously. On seven datasets including five medical datasets
and two infrared image datasets, our SRSNet consistently achieves the best
segmentation results. The code is released at this https URL

本文提出了一种用于弱目标图像分割（包括医学图像分割和红外图像分割）的高性能深度神经网络。为此，本文分析了现有的动态卷积，并提出了动态参数卷积（DPConv）。此外，本文从 DPConv 的角度重新评估了重建任务与分割任务之间的关系，提出了一种名为 Siamese Reconstruction-Segmentation Network（SRSNet）的双网络模型。所提出的模型不仅是一个通用网络，而且通过利用重建任务来提高分割性能，而无需改变其结构。此外，随着重建网络的训练数据量增加，分割网络的性能也得到同步提高。在包括五个医学数据集和两个红外图像数据集在内的七个数据集上，我们的 SRSNet 始终取得了最佳的分割结果。代码可在此 https URL 上获得。