Détection du carcinome basocellulaire dans des images OCT plein champ utilisant un réseau de neurones convolutif
| Autor: | John R. Durkin, Vannary Meas-Yedid, Jean-Christophe Olivo-Marin, C. Boceara, Diana Mandache, E. Dalimier |
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| Přispěvatelé: | Analyse d'images biologiques - Biological Image Analysis (BIA), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), LLTech SAS Paris, Drexel University |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
medicine.medical_treatment 02 engineering and technology Convolutional neural network [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences 0302 clinical medicine Optical coherence tomography convolutional neural networks 0202 electrical engineering electronic engineering information engineering medicine Mohs surgery [INFO.INFO-IM]Computer Science [cs]/Medical Imaging Basal cell carcinoma 030212 general & internal medicine Generalized normal distribution medicine.diagnostic_test business.industry Deep learning Digital pathology 020207 software engineering Pattern recognition Optical Biopsy medicine.disease Cancer cell nonmelanoma skin cancer Artificial intelligence business digital pathology Classifier (UML) full field optical coherence tomography |
| Zdroj: | IEEE 15th International Symposium on Biomedical Imaging IEEE 15th International Symposium on Biomedical Imaging, Apr 2018, Washington, United States. ⟨10.1109/ISBI.2018.8363689⟩ ISBI |
| DOI: | 10.1109/ISBI.2018.8363689⟩ |
| Popis: | International audience; 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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