Abstract
The emergence of computational pathology comes with a demand to extract more and more information from each tissue sample. Such information extraction often requires the segmentation of numerous histological objects (e.g., cell nuclei, glands, etc.) in histological slide images, a task for which deep learning algorithms have demonstrated their effectiveness. However, these algorithms require many training examples to be efficient and robust. For this purpose, pathologists must manually segment hundreds or even thousands of objects in histological images, i.e., a long, tedious and potentially biased task. The present paper aims to review strategies that could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning. This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning. In addition, we describe alternative learning strategies that can use imperfect annotations. Adding real data with high-quality annotations to the training set is a safe way to improve the performance of a well configured deep neural network. However, the present review provides new perspectives through the use of artificially generated data and/or imperfect annotations, in addition to transfer learning opportunities.
Highlights
More and more information is needed for diagnosis and therapeutic decision-making, especially in the context of “personalized medicine.” As a result, pathologists are expressing a growing demand for the automation of their most recurrent tasks and for a more complex set of analyses required for their research activities
We focus on Generative Adversarial Networks (GAN)-based data augmentation useful for histological image segmentation
The authors noted that GAN augmentation provides an efficient tool for interpolating within the training data distribution. It cannot extrapolate beyond its extremes without the aid of standard geometric augmentation. These results suggest a synergic effect between standard data augmentation and GAN augmentation
Summary
More and more information is needed for diagnosis and therapeutic decision-making, especially in the context of “personalized medicine.” As a result, pathologists are expressing a growing demand for the automation of their most recurrent tasks and for a more complex set of analyses required for their research activities. In addition to biomarker evaluation, computational pathology aims to characterize a disease at the molecular, individual and population levels This approach transforms those data into knowledge that can be directly used by pathologists and clinicians. An important contribution to computational pathology is computational histology or “histomics,” which aims to extract as much information as possible from digital histological slides [3] Histomics makes it possible to characterize the histological manifestation of a disease by taking into account the morphological, spatial and microenvironmental context. Deep learning is known to be a data-hungry method, requiring much more training data than standard machine learning approaches [8] Collecting such data for histomics applications can be problematic, for image segmentation, which requires manual annotations from pathologists, a rare and expensive resource. We discuss tasks that remain to be done to make the most of these strategies to minimize the need for expert supervision
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