Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.
Pattern Recognit
; 86: 188-200, 2019 Feb.
Article
em En
| MEDLINE
| ID: mdl-30631215
ABSTRACT
We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully- supervised annotation cost.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
Pattern Recognit
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos