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.

A Convolutional Neural Network Pipeline For Multi-Temporal Retinal Image Registration.

A sequence of images is usually captured to observe the change of health status in medical diagnosis. However, an image sequence taken over year usually suffers from severe deformation, making it time-consuming for physicians to match corresponding patterns. In this paper, we propose a coarse-to-fine pipeline for retinal image registration based on convolutional neural network. By leveraging the three components of the pipeline: feature matching, outlier rejection, and local registration, we recover the deformation and accurately align multi-temporal image sequences. Experimental results show that the proposed network is robust to severe deformation as well as illumination and contrast variations. With the proposed registration pipeline, the change of image patterns over time can be identified through visual analysis.

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80% for mouse skin. Using the sub-micron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications.