Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration

Abstract

We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…Recent works have extended the CNN framework to complex medical image analysis, such as retinal blood vessels segmentation [39,40], retinal hemorrhage detection [41], brain tumor segmentation [42], and cerebral microbleeds detection [43]. Very recently, a CNN based method was proposed [44] as an alternative to classic machine learning methods [5] for classification of normal and pathologic OCT images. In addition, the CNN model has also been applied to analyze OCT images of skin, aiming to characterize heathy skin and healing wounds [45].…”

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

“…Through this data abstraction, often times relevant complex mathematical representations of images are determined that would otherwise be challenging to determine by the naked eye. Previous work has shown the efficacy of using CNN to diagnose various ophthalmic and skin disease pathologies suggesting that CNN could be used classify OCT images of varied disease pathologies provided a substantially large labeled data set . Here, we describe the use of a CNN in assessment of HNSCC margins, in hopes that this processing pipeline may be used in future OCT HNSCC investigations.…”

“…Previous work has shown the efficacy of using CNN to diagnose various ophthalmic and skin disease pathologies suggesting that CNN could be used classify OCT images of varied disease pathologies provided a substantially large labeled data set. [35][36][37] Here, we describe the use of a CNN in assessment of HNSCC margins, in hopes that this processing pipeline may be used in future OCT HNSCC investigations.…”

The European Eye Epidemiology spectral‐domain optical coherence tomography classification of macular diseases for epidemiological studies

“…Recently, many papers on automated image analysis in several retinal diseases have been published (Bogunovic et al 2017;Karri et al 2017;Khalid et al 2017). For instance, in AMD automated detection of drusen, geographic atrophy or subretinal fluid has been developed (Wintergerst et al 2017).…”