  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.
