In recent years, convolutional neural networks for semantic segmentation of
breast ultrasound (BUS) images have shown great success; however, two major
challenges still exist. 1) Most current approaches inherently lack the ability
to utilize tissue anatomy, resulting in misclassified image regions. 2) They
struggle to produce accurate boundaries due to the repeated down-sampling
operations. To address these issues, we propose a novel breast anatomy-aware
network for capturing fine image details and a new smoothness term that encodes
breast anatomy. It incorporates context information across multiple spatial
scales to generate more accurate semantic boundaries. Extensive experiments are
conducted to compare the proposed method and eight state-of-the-art approaches
using a BUS dataset with 325 images. The results demonstrate the proposed
method significantly improves the segmentation of the muscle, mammary, and
tumor classes and produces more accurate fine details of tissue boundaries.

通过提出一种新的结构感知网络和平滑度项，以更好地利用组织解剖学信息并生成更准确的语义边界，对乳腺超声图像进行分割，从而显著改善了肌肉、乳腺和肿瘤类别的分割结果，并产生更准确的组织边界细节。

A2DMN: 解剖感知扩展多尺度网络用于乳腺超声语义分割

A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound  Semantic Segmentation

The process of using one image to guide the filtering process of another one
is called Guided Image Filtering (GIF). The main challenge of GIF is the
structure inconsistency between the guidance image and the target image.
Besides, noise in the target image is also a challenging issue especially when
it is heavy. In this paper, we propose a general framework for Robust Guided
Image Filtering (RGIF), which contains a data term and a smoothness term, to
solve the two issues mentioned above. The data term makes our model
simultaneously denoise the target image and perform GIF which is robust against
the heavy noise. The smoothness term is able to make use of the property of
both the guidance image and the target image which is robust against the
structure inconsistency. While the resulting model is highly non-convex, it can
be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in
an efficient manner. For challenging applications such as guided depth map
upsampling, we further develop a data-driven parameter optimization scheme to
properly determine the parameter in our model. This optimization scheme can
help to preserve small structures and sharp depth edges even for a large
upsampling factor (8x for example). Moreover, the specially designed structure
of the data term and the smoothness term makes our model perform well in
edge-preserving smoothing for single-image tasks (i.e., the guidance image is
the target image itself). This paper is an extension of our previous work [1],
[2].

本文提出了一种基于数据项和光滑项的 Robust Guided Image Filtering（RGIF）通用框架，旨在解决引导图像与目标图像之间的结构不一致性以及目标图像中噪声的问题。该模型可同时去噪目标图像和执行对噪声具有鲁棒性的 GIF 操作，并具有良好的边缘保持平滑处理效果。通过提出的迭代加权最小二乘法（IRLS）方法来解决高度非凸问题，进一步开发基于数据驱动的参数优化方案，以适当确定模型中参数。