The Utility of Unsupervised Machine Learning in Anatomic Pathology
| Autor: | Ewen D McAlpine, Turgay Celik, Pamela Michelow |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Computer science Machine learning computer.software_genre Synthetic data 03 medical and health sciences 0302 clinical medicine Classifier (linguistics) Humans Cluster analysis business.industry Supervised learning Digital pathology General Medicine Autoencoder ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Data point 030220 oncology & carcinogenesis Unsupervised learning Artificial intelligence Supervised Machine Learning business computer Algorithms Unsupervised Machine Learning |
| Zdroj: | American journal of clinical pathology. 157(1) |
| ISSN: | 1943-7722 |
| Popis: | Objectives Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laborious process. Unsupervised learning, which does not require annotated data, possesses the potential to assist with this challenge. This review aims to introduce the concept of unsupervised learning and illustrate how clustering, generative adversarial networks (GANs) and autoencoders have the potential to address the lack of annotated data in anatomic pathology. Methods A review of unsupervised learning with examples from the literature was carried out. Results Clustering can be used as part of semisupervised learning where labels are propagated from a subset of annotated data points to remaining unlabeled data points in a dataset. GANs may assist by generating large amounts of synthetic data and performing color normalization. Autoencoders allow training of a network on a large, unlabeled dataset and transferring learned representations to a classifier using a smaller, labeled subset (unsupervised pretraining). Conclusions Unsupervised machine learning techniques such as clustering, GANs, and autoencoders, used individually or in combination, may help address the lack of annotated data in pathology and improve the process of developing supervised learning models. |
| Databáze: | OpenAIRE |
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