Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
For diagnosing melanoma, hematoxylin and eosin (H&E) stained tissue slides remains the gold standard. These images contain quantitative information in different magnifications. In the present study, we investigated whether deep convolutional neural networks can extract structural features of com...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
12.04.2019
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Subjects | |
Online Access | Get full text |
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Summary: | For diagnosing melanoma, hematoxylin and eosin (H&E) stained tissue slides
remains the gold standard. These images contain quantitative information in
different magnifications. In the present study, we investigated whether deep
convolutional neural networks can extract structural features of complex
tissues directly from these massive size images in a patched way. In order to
face the challenge arise from morphological diversity in histopathological
slides, we built a multicenter database of 2241 digital whole-slide images from
1321 patients from 2008 to 2018. We trained both ResNet50 and Vgg19 using over
9.95 million patches by transferring learning, and test performance with two
kinds of critical classifications: malignant melanomas versus benign nevi in
separate and mixed magnification; and distinguish among nevi in maximum
magnification. The CNNs achieves superior performance across both tasks,
demonstrating an AI capable of classifying skin cancer in the analysis from
histopathological images. For making the classifications reasonable, the
visualization of CNN representations is furthermore used to identify cells
between melanoma and nevi. Regions of interest (ROI) are also located which are
significantly helpful, giving pathologists more support of correctly diagnosis. |
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DOI: | 10.48550/arxiv.1904.06156 |