A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
| Autor: | Sharon Nofech-Mozes, Anne L. Martel, Sherine Salama, Mohammad Peikari |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Structure (mathematical logic) Pathology medicine.medical_specialty Multidisciplinary Contextual image classification Computer science Science 02 engineering and technology Semi-supervised learning Disease cluster Article Support vector machine 03 medical and health sciences 030104 developmental biology ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Decision boundary medicine Medicine 020201 artificial intelligence & image processing Cluster analysis |
| Zdroj: | Scientific Reports Scientific Reports, Vol 8, Iss 1, Pp 1-13 (2018) |
| ISSN: | 2045-2322 |
| Popis: | Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data. |
| Databáze: | OpenAIRE |
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