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。

基于 GRU 网络的乳腺组织病理图像分割

GRU-Net for breast histopathology image segmentation

Multi-modal learning adeptly integrates visual and textual data, but its
application to histopathology image and text analysis remains challenging,
particularly with large, high-resolution images like gigapixel Whole Slide
Images (WSIs). Current methods typically rely on manual region labeling or
multi-stage learning to assemble local representations (e.g., patch-level) into
global features (e.g., slide-level). However, there is no effective way to
integrate multi-scale image representations with text data in a seamless
end-to-end process. In this study, we introduce Multi-Level Text-Guided
Representation End-to-End Learning (mTREE). This novel text-guided approach
effectively captures multi-scale WSI representations by utilizing information
from accompanying textual pathology information. mTREE innovatively combines -
the localization of key areas (global-to-local) and the development of a
WSI-level image-text representation (local-to-global) - into a unified,
end-to-end learning framework. In this model, textual information serves a dual
purpose: firstly, functioning as an attention map to accurately identify key
areas, and secondly, acting as a conduit for integrating textual features into
the comprehensive representation of the image. Our study demonstrates the
effectiveness of mTREE through quantitative analyses in two image-related
tasks: classification and survival prediction, showcasing its remarkable
superiority over baselines.

该研究介绍了一种称为 mTREE 的多层次文本引导端到端学习方法，它有效地捕获了多尺度病理组织学图像表示，并将文本信息与图像综合表示相结合，展示了其在分类和生存预测等任务中相对于传统方法的优越性。