  Annotating cancerous regions in whole-slide images (WSIs) of pathology
samples plays a critical role in clinical diagnosis, biomedical research, and
machine learning algorithms development. However, generating exhaustive and
accurate annotations is labor-intensive, challenging, and costly. Drawing only
coarse and approximate annotations is a much easier task, less costly, and it
alleviates pathologists' workload. In this paper, we study the problem of
refining these approximate annotations in digital pathology to obtain more
accurate ones. Some previous works have explored obtaining machine learning
models from these inaccurate annotations, but few of them tackle the refinement
problem where the mislabeled regions should be explicitly identified and
corrected, and all of them require a -- often very large -- number of training
samples. We present a method, named Label Cleaning Multiple Instance Learning
(LC-MIL), to refine coarse annotations on a single WSI without the need of
external training data. Patches cropped from a WSI with inaccurate labels are
processed jointly within a multiple instance learning framework, mitigating
their impact on the predictive model and refining the segmentation. Our
experiments on a heterogeneous WSI set with breast cancer lymph node
metastasis, liver cancer, and colorectal cancer samples show that LC-MIL
significantly refines the coarse annotations, outperforming state-of-the-art
alternatives, even while learning from a single slide. Moreover, we demonstrate
how real annotations drawn by pathologists can be efficiently refined and
improved by the proposed approach. All these results demonstrate that LC-MIL is
a promising, light-weight tool to provide fine-grained annotations from
coarsely annotated pathology sets.
