Basal cell carcinoma detection in full field OCT images using convolutional neural networks

In this paper we introduce a new application that exploits the emerging imaging modality of full field optical coherence tomography (FFOCT) as a means of optical biopsy. The objective is to build a computer-aided diagnosis (CAD) tool that can speed up the detection of tumoral areas in skin excisions...

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Bibliographic Details
Main Authors: Mandache, D., Dalimier, E., Durkin, J. R., Boceara, C., Olivo-Marin, J.-C., Meas-Yedid, V.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical optical imaging
Computer architecture
Convolutional neural networks
digital pathology
full field optical coherence tomography
Machine learning
nonmelanoma skin cancer
Optical imaging
Skin
Training
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Summary:In this paper we introduce a new application that exploits the emerging imaging modality of full field optical coherence tomography (FFOCT) as a means of optical biopsy. The objective is to build a computer-aided diagnosis (CAD) tool that can speed up the detection of tumoral areas in skin excisions resulting from Mohs surgery. Since there is little prior knowledge about the appearance of cancer cell morphology in this type of imagery, deep learning techniques are applied. Using convolutional neural networks (CNN), we train a feature extractor able to find representative characteristics for FFOCT data and a classifier that learns a generalized distribution of the data. With a dataset of 40 high-resolution images, we obtained a classification accuracy of 95.93%.
ISSN:1945-8452
DOI:10.1109/ISBI.2018.8363689