An exploratory study of one-shot learning using Siamese convolutional neural network for histopathology image classification in breast cancer from few data examples
| Autor: | Fabian Cano, Angel Cruz-Roa |
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| Rok vydání: | 2020 |
| Předmět: |
Similarity (geometry)
Contextual image classification Computer science business.industry Generalization Digital pathology Machine learning computer.software_genre One-shot learning Convolutional neural network Class (biology) Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer |
| Zdroj: | 15th International Symposium on Medical Information Processing and Analysis. |
| Popis: | Convolutional Neural Networks have been successfully used in several tasks in the last decade, but this kind of supervised method requires a large number of annotated examples to obtain good results. However, in biomedical domains, such as digital pathology, the amount of annotated samples is very reduced and imbalanced. On the other hand, models trained using supervised methods need to be retrained when a new class (with its samples) is introduced. This process is computationally expensive and can be difficult when you don’t have a lot of examples for the same class. In order to address these issues, there are novel approaches that use a few number of annotated examples to achieve an acceptable or good generalization capability of the model. One of these methods is One-shot Learning, which is intended to classify a set of data from different classes from one or a few number of annotated examples, and which allows to incorporate new data from new classes without re-training the model. This work explores a method to classify samples of tissues in breast cancer histopathology images by means of similarity between pairs of image samples using a Siamese Convolutional Neural Networks, which achieved a 90.83% of accuracy test. |
| Databáze: | OpenAIRE |
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