TY - GEN
T1 - Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network
AU - Wodzinski, Marek
AU - Pajak, Miroslawa
AU - Skalski, Andrzej
AU - Witkowski, Alexander
AU - Pellacani, Giovanni
AU - Ludzik, Joanna
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.
AB - Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.
KW - convolutional neural network
KW - deep learning
KW - melanoma
KW - reflectance confocal microscopy
KW - skin lesion
UR - http://www.scopus.com/inward/record.url?scp=85091044408&partnerID=8YFLogxK
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U2 - 10.1109/EMBC44109.2020.9176557
DO - 10.1109/EMBC44109.2020.9176557
M3 - Conference contribution
AN - SCOPUS:85091044408
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1824
EP - 1827
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -