Name
Title | ||
|---|---|---|
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. |
Abstract | ||
|---|---|---|
Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of histopathological whole-slide images (WSIs) is challenging owing to the wide range of appearances of benign lesions and the visual similarity of ductal carcinoma in-situ (DCIS) to invasive lesions at the cellular level. Consequently, analysis of tissue at high resolutions with a large contextual area is necessary. We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of hematoxylin and eosin stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of nonmalignant and malignant slides and obtains a three-class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potential for routine diagnostics. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/1.JMI.4.4.044504 | JOURNAL OF MEDICAL IMAGING |
Keywords | Field | DocType |
deep learning,convolutional neural networks,breast cancer,histopathology,context-aware CNN | H&E stain,Ductal carcinoma,Pattern recognition,Binary classification,Breast cancer,Computer science,Convolutional neural network,Histopathology,Breast lesion,Artificial intelligence,Deep learning,Pathology | Journal |
Volume | Issue | ISSN |
4 | 4 | 2329-4302 |
Citations | PageRank | References |
6 | 0.61 | 24 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Babak Ehteshami Bejnordi | 1 | 720 | 30.27 |
| Guido C. A. Zuidhof | 2 | 6 | 0.61 |
| Maschenka Balkenhol | 3 | 31 | 2.24 |
| m hermsen | 4 | 20 | 2.26 |
| p bult | 5 | 23 | 2.35 |
| Bram van Ginneken | 6 | 4979 | 307.23 |
| Nico Karssemeijer | 7 | 992 | 122.49 |
| Geert Litjens | 8 | 996 | 50.79 |
| j a w m van der laak | 9 | 702 | 29.53 |