Histopathological image analysis is an essential process for the discovery of
diseases such as cancer. However, it is challenging to train CNN on whole slide
images (WSIs) of gigapixel resolution considering the available memory
capacity. Most of the previous works divide high resolution WSIs into small
image patches and separately input them into the model to classify it as a
tumor or a normal tissue. However, patch-based classification uses only
patch-scale local information but ignores the relationship between neighboring
patches. If we consider the relationship of neighboring patches and global
features, we can improve the classification performance. In this paper, we
propose a new model structure combining the patch-based classification model
and whole slide-scale segmentation model in order to improve the prediction
performance of automatic pathological diagnosis. We extract patch features from
the classification model and input them into the segmentation model to obtain a
whole slide tumor probability heatmap. The classification model considers
patch-scale local features, and the segmentation model can take global
information into account. We also propose a new optimization method that
retains gradient information and trains the model partially for end-to-end
learning with limited GPU memory capacity. We apply our method to the
tumor/normal prediction on WSIs and the classification performance is improved
compared with the conventional patch-based method.

本论文提出了一种新的模型结构，将基于补丁的分类模型和整个切片比例的分割模型相结合，以提高自动病理诊断的预测性能，并通过优化方法，使模型部分地进行端到端学习，应用于 WSI 的肿瘤 / 正常预测，与传统基于补丁的方法相比，分类的性能得到了提高。