SHISRCNet：用于低分辨率乳腺癌组织病理学图像的超分辨率与分类网络
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
AI总结：
针对乳腺癌病理图像低分辨率分类和多尺度特征提取不足的问题，提出SHISRCNet，通过联合训练超分辨率与分类模块，在低分辨率输入下达到接近SOTA方法的性能。
摘要：
乳腺癌被称为女性杀手，其快速识别和准确诊断对患者具有重大意义。目前已提出许多乳腺癌组织病理学图像分类方法，但它们仍面临两个问题。（1）这些方法只能处理高分辨率（HR）图像。然而，受限于硬件条件，数字切片扫描仪采集的常是低分辨率（LR）图像。与HR图像相比，LR图像常丢失纹理等关键特征，严重影响诊断准确性。（2）现有方法感受野固定，无法很好地提取和融合不同放大倍数图像的多尺度特征。为填补这些空白，我们提出了单张组织病理学图像超分辨率分类网络（SHISRCNet），包含超分辨率（SR）和分类（CF）两个模块。SR模块将LR图像重建为SR图像。CF模块提取并融合SR图像的多尺度特征以进行分类。在训练阶段，我们将HR图像引入CF模块以增强SHISRCNet的性能。最后，通过两个模块的联合训练，LR图像的超分辨率与分类被整合到我们的模型中。实验结果表明，我们的方法效果接近以HR图像作为输入的SOTA方法。
英文摘要：
The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they still suffer from two problems. (1) These methods can only hand high-resolution (HR) images. However, the low-resolution (LR) images are often collected by the digital slide scanner with limited hardware conditions. Compared with HR images, LR images often lose some key features like texture, which deeply affects the accuracy of diagnosis. (2) The existing methods have fixed receptive fields, so they can not extract and fuse multi-scale features well for images with different magnification factors. To fill these gaps, we present a \textbf{S}ingle \textbf{H}istopathological \textbf{I}mage \textbf{S}uper-\textbf{R}esolution \textbf{C}lassification network (SHISRCNet), which consists of two modules: Super-Resolution (SR) and Classification (CF) modules. SR module reconstructs LR images into SR ones. CF module extracts and fuses the multi-scale features of SR images for classification. In the training stage, we introduce HR images into the CF module to enhance SHISRCNet's performance. Finally, through the joint training of these two modules, super-resolution and classified of LR images are integrated into our model. The experimental results demonstrate that the effects of our method are close to the SOTA methods with taking HR images as inputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
