Combining Datasets with Different Label Sets for Improved Nucleus
  Segmentation and Classification
Amruta Parulekar Utkarsh Kanwat Ravi Kant Gupta Medha Chippa Thomas Jacob Tripti Bameta Swapnil Rane Amit Sethi
Segmentation and classification of cell nuclei in histopathology images using
deep neural networks (DNNs) can save pathologists' time for diagnosing various
diseases, including cancers, by automating cell counting and morphometric
assessments. It is now well-known that the accuracy of DNNs increases with the
sizes of annotated datasets available for training. Although multiple datasets
of histopathology images with nuclear annotations and class labels have been
made publicly available, the set of class labels differ across these datasets.
We propose a method to train DNNs for instance segmentation and classification
on multiple datasets where the set of classes across the datasets are related
but not the same. Specifically, our method is designed to utilize a
coarse-to-fine class hierarchy, where the set of classes labeled and annotated
in a dataset can be at any level of the hierarchy, as long as the classes are
mutually exclusive. Within a dataset, the set of classes need not even be at
the same level of the class hierarchy tree. 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. Furthermore, generalization to previously unseen
datasets also improves by combining multiple other datasets with different sets
of classes for training. The improvement is both qualitative and quantitative.
The proposed method can be adapted for various loss functions, DNN
architectures, and application domains.