Title | ||
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Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2020 | 10.3390/jimaging6090082 | JOURNAL OF IMAGING |
Keywords | DocType | Volume |
digital pathology,whole slide image processing,multiple instance learning,convolutional neural networks,deep learning classification,HER2 | Journal | 6 |
Issue | ISSN | Citations |
9 | 2313-433X | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
David La Barbera | 1 | 1 | 0.70 |
António Polónia | 2 | 28 | 2.16 |
Kevin Roitero | 3 | 30 | 13.74 |
Eduardo Conde-Sousa | 4 | 4 | 1.10 |
Vincenzo Della Mea | 5 | 2 | 2.06 |