Share Email Print
cover

Proceedings Paper • new

An exploratory study of one-shot learning using Siamese convolutional neural network for histopathology image classification in breast cancer from few data examples
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300A (3 January 2020); doi: 10.1117/12.2546488
Show Author Affiliations
Fabian Cano, Univ. de los Llanos (Colombia)
Angel Cruz-Roa, Univ. de los Llanos (Colombia)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

© SPIE. Terms of Use
Back to Top