Abstract
Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. However, there has been limited work that addresses the problem of accurately classifying breast biopsy tissue with hematoxylin and eosin stained images into different histological grades. We propose a system which can automatically classify breast cancer histology images into four classes, namely normal tissues, benign lesion, in situ carcinoma and invasive carcinoma. Our framework uses a Convolutional Neural Network (CNN) with a hierarchical loss, where failing to distinguish between carcinoma and non-carcinoma is penalized more than failing to distinguish between normal and benign or between in situ and invasive carcinoma. The network also includes a patch-wise design with global pooling directly on input images. By incorporating the hierarchical and global information of the input images, our framework can outperform the previous system by a large margin.
Original language | English |
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Title of host publication | Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings |
Editors | Bart ter Haar Romeny, Fakhri Karray, Aurelio Campilho |
Pages | 745-753 |
Number of pages | 9 |
DOIs | |
State | Published - 2018 |
Event | 15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal Duration: Jun 27 2018 → Jun 29 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10882 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 15th International Conference on Image Analysis and Recognition, ICIAR 2018 |
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Country/Territory | Portugal |
City | Povoa de Varzim |
Period | 6/27/18 → 6/29/18 |
Bibliographical note
Publisher Copyright:© 2018, Springer International Publishing AG, part of Springer Nature.
Keywords
- Breast cancer
- Convolutional Neural Networks
- Hierarchical loss
- Histopathology
- Image classification
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science