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

    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 ...

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