ReviewFrontiers in medicine2019
Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images.
Review in Frontiers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Self-supervised learning for data augmentation in histopathology image segmentation.Scientific reports · 2025Article
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Generative Modeling of Histology Tissue Reduces Human Annotation Effort for Segmentation Model Development.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs.Journal of systems science and complexity · 2023Article
- A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.Communications medicine · 2022Article
- Quick Annotator: an open-source digital pathology based rapid image annotation tool.The journal of pathology. Clinical research · 2021Article
- Artificial intelligence for automating the measurement of histologic image biomarkers.The Journal of clinical investigation · 2021Article
- U-shaped GAN for Semi-Supervised Learning and Unsupervised Domain Adaptation in High Resolution Chest Radiograph Segmentation.Frontiers in medicine · 2021Article
- Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data.Frontiers in digital health · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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