Slide2Graph provides a way to analyze whole slide images using a graph convolutional neural network. In our framework, a whole slide image can be viewed as a graph where each tissue patch represents a node in the graph. Our Slide2Graph takes both local patches' features and global structural-and-positional information of the patches as inputs to construct a computational graph of a histopathology image. Our study shows that the structural and positional information is a critical component of Slide2Graph in achieving higher performance than a conventional deep convolutional neural network that takes only patch-based features into account.
To get the high-dimensional representations & coordinates of patches in one whole slide images, SlidePreprocessing is used to extract tissues and generate small fixed-size patches from whole slide images. After generating the patches, you can train a patch-level ResNet as a feature extractor using the patches. Notably, you can use a ResNet model pretrained on ImageNet as an alternative feature extractor; however, a ResNet trained on specific histopathological patches performs generally better. Finally, the ResNet without the last FC layer will be utilized to generate the high-dimensional representations for the small fixed-size patch images.
Other libraries and tools can be used to preprocess the entire slide image set and generate patches. The generated patches need to be named as 'a_b_c_d' where a & b are the corresponding tissues' positional indexes in the whole slide images, and c & d are the positional indexes of patches in the corresponding tissue. In addition, the patches need to be saved in separate folders named after the whole slide images.
Run python generate_graphs.py to generate graphs from the patches' coordinates and the extracted high dimensional features of whole slide images. The default setting is creating edges between every node and its four neartest nodes. The edges will be weighted by the reciprocal euclidean distance.
Run python main.py to train the model. Some parameters can be modifed in config.py
The patches images are saved as the following structure. patches are named after postion indexes.
ROOT
|- DATASET
|- SLIDENAME
|- PATCH_CLASS (e.g. Positive)
|- patches.png
The information of patches are stored in a list of dictionaries, like [{index:WSI_name}, {index:class}, {index:split}]. You can modify the val_raw_pkl argument in the config.py if nesscessary.
