Classification of squamous cell carcinoma from FF-OCT images: Data selection and progressive model construction

Author:

Ho Chi-Jui,Calderon-Delgado ManuelORCID,Lin Ming-Yi,Tjiu Jeng-Wei,Huang Sheng-Lung,Chen Homer H.

Funder

Ministry of Science and Technology, Taiwan

National Taiwan University

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference47 articles.

1. A survey of cross-validation procedures for model selection;Arlot;Stat. Surv.,2010

2. OCT-MoS, a dataset of mouse skin squamous cell carcinoma stages by full-field optical coherence tomography;Calderon-Delgado;Image Data Resource,2021

3. The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology;Chan;Int. J. Surg. Pathol.,2014

4. Clevert, D.A., Unterthiner, T., Hochreiter, S., 2015.Fast and accurate deep network learning by exponential linear units (elus). arXiv: 〈http://arXiv.org/abs/arXiv:1511.07289〉.

5. Full-field optical coherence tomography: a new technology for 3d high-resolution skin imaging;Dalimier;Dermatology,2012

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