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

We investigate the speed and performance of squamous cell carcinoma (SCC) classification from full-field optical coherence tomography (FF-OCT) images based on the convolutional neural network (CNN). Due to the unique characteristics of SCC features, the high variety of CNN, and the high volume of our 3D FF-OCT dataset, progressive model construction is a time-consuming process. To address the issue, we develop a training strategy for data selection that makes model training 16 times faster by exploiting the dependency between images and the knowledge of SCC feature distribution. The speedup makes progressive model construction computationally feasible. Our approach further refines the regularization, channel attention, and optimization mechanism of SCC classifier and improves the accuracy of SCC classification to 87.12% at the image level and 90.10% at the tomogram level. The results are obtained by testing the proposed approach on an FF-OCT dataset with over one million mouse skin images. [Display omitted] • Exploit the knowledge of FF-OCT images and squamous cell carcinoma to enhance the efficiency of model training. • Propose an effective training strategy that speeds up progressive model construction by a factor of 16. • Incorporate channel attention and soft-labeling into a CNN-based classifier for SCC of mouse skin. • Integrated with an FF-OCT device, the classifier yields 90.10% accuracy. [ABSTRACT FROM AUTHOR]