  Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their
conclusion based on observed cell and tissue structure on histology slides.
Rapid development in machine learning, especially deep learning have
established robust and accurate classifiers. They are being used to analyze
histopathological slides and assist pathologists in diagnosis. Most machine
learning systems rely heavily on annotated data sets to gain experiences and
knowledge to correctly and accurately perform various tasks such as
classification and segmentation. This work investigates different granularity
of annotations in histopathological data set including image-wise, bounding
box, ellipse-wise, and pixel-wise to verify the influence of annotation in
pathological slide on deep learning models. We design corresponding experiments
to test classification and segmentation performance of deep learning models
based on annotations with different annotation granularity. In classification,
state-of-the-art deep learning-based classifiers perform better when trained by
pixel-wise annotation dataset. On average, precision, recall and F1-score
improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that
finer granularity annotations are better utilized by deep learning algorithms
in classification tasks. Similarly, semantic segmentation algorithms can
achieve 8.33% better segmentation accuracy when trained by pixel-wise
annotations. Our study shows not only that finer-grained annotation can improve
the performance of deep learning models, but also help extracts more accurate
phenotypic information from histopathological slides. Intelligence systems
trained on granular annotations may help pathologists inspecting certain
regions for better diagnosis. The compartmentalized prediction approach similar
to this work may contribute to phenotype and genotype association studies.
