A Guided Spatial Transformer Network for Histology Cell Differentiation
This work addresses the tedious and error-prone task of cell identification in diagnostic histopathology, offering a semi-automated solution to support pathologists, though it appears incremental as it builds on existing deep learning methods with a novel dataset.
The paper tackled the problem of automatic classification and segmentation of cells and mitotic figures in histopathology, achieving a mean accuracy of 91.45% in a five-fold cross-validation using a deep convolutional network with a Spatial Transformer Network trained on a dataset ten times larger than previous ones.
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45%. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.