Next Article in Journal
Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation
Next Article in Special Issue
Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
Previous Article in Journal
On Computational Aspects of Krawtchouk Polynomials for High Orders
Previous Article in Special Issue
Full 3D Microwave Breast Imaging Using a Deep-Learning Technique
Open AccessArticle

Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning

1
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
2
Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, 4169-007 Porto, Portugal
3
i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4169-007 Porto, Portugal
4
INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4169-007 Porto, Portugal
*
Author to whom correspondence should be addressed.
J. Imaging 2020, 6(9), 82; https://doi.org/10.3390/jimaging6090082
Received: 1 July 2020 / Revised: 14 August 2020 / Accepted: 18 August 2020 / Published: 23 August 2020
(This article belongs to the Special Issue Deep Learning on Medical Image Analysis)
Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin–Eosin slides, which partly mimics the pathologist’s behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score. View Full-Text
Keywords: digital pathology; whole slide image processing; multiple instance learning; convolutional neural networks; deep learning classification; HER2 digital pathology; whole slide image processing; multiple instance learning; convolutional neural networks; deep learning classification; HER2
Show Figures

Figure 1

MDPI and ACS Style

La Barbera, D.; Polónia, A.; Roitero, K.; Conde-Sousa, E.; Della Mea, V. Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning. J. Imaging 2020, 6, 82.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop