Breast cancer is a major global health concern. Pathologists face challenges
in analyzing complex features from pathological images, which is a
time-consuming and labor-intensive task. Therefore, efficient computer-based
diagnostic tools are needed for early detection and treatment planning. This
paper presents a modified version of MultiResU-Net for histopathology image
segmentation, which is selected as the backbone for its ability to analyze and
segment complex features at multiple scales and ensure effective feature flow
via skip connections. The modified version also utilizes the Gaussian
distribution-based Attention Module (GdAM) to incorporate
histopathology-relevant text information in a Gaussian distribution. The
sampled features from the Gaussian text feature-guided distribution highlight
specific spatial regions based on prior knowledge. Finally, using the
Controlled Dense Residual Block (CDRB) on skip connections of MultiResU-Net,
the information is transferred from the encoder layers to the decoder layers in
a controlled manner using a scaling parameter derived from the extracted
spatial features. We validate our approach on two diverse breast cancer
histopathology image datasets: TNBC and MonuSeg, demonstrating superior
segmentation performance compared to state-of-the-art methods. The code for our
proposed model is available on this https URL

本研究提出了一种改进的 MultiResU-Net 用于组织病理学图像分割，该模型通过多尺度分析和分割复杂特征，并通过跳跃连接确保有效的特征传递；改进版本还利用基于高斯分布的注意力机制在高斯分布中合并与组织病理学相关的文本信息；最后，通过对 MultiResU-Net 的跳跃连接采用控制稠密残差块，以从编码器层向解码器层传递信息，使用从提取的空间特征导出的缩放参数，以控制的方式进行传递。验证实验表明，在 TNBC 和 MonuSeg 等两种不同类型的乳腺癌组织病理学图像数据集上，与现有方法相比，该方法展现出卓越的分割性能。该模型的代码可在该 https URL 找到。