Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
Segmentation and classification of cell nuclei using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including cancers. The accuracy of DNNs increases with the sizes of annotated datasets available for training. The available public datasets with nuclear annotations and labels differ in their class label sets. We propose a method to train DNNs on multiple datasets where the set of classes across the datasets are related but not the same. Our method is designed to utilize class hierarchies, where the set of classes in a dataset can be at any level of the hierarchy. Our results demonstrate that segmentation and classification metrics for the class set used by the test split of a dataset can improve by pre-training on another dataset that may even have a different set of classes due to the expansion of the training set enabled by our method.