Learning deep similarity in fundus photography

Chudzik, Piotr and Al-Diri, Bashir and Caliva, Francesco and Ometto, Giovanni and Hunter, Andrew (2017) Learning deep similarity in fundus photography. In: SPIE Medical Imaging, 11 - 16 February 2017, Orlando, Florida, United States.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

Similarity learning is one of the most fundamental tasks in image analysis. The ability to extract similar images in the medical domain as part of content-based image retrieval (CBIR) systems has been researched for many years. The vast majority of methods used in CBIR systems are based on hand-crafted feature descriptors. The approximation of a similarity mapping for medical images is difficult due to the big variety of pixel-level structures of interest. In fundus photography (FP) analysis, a subtle difference in e.g. lesions and vessels shape and size can result in a different diagnosis. In this work, we demonstrated how to learn a similarity function for image patches derived directly from FP image data without the need of manually designed feature descriptors. We used a convolutional neural network (CNN) with a novel architecture adapted for similarity learning to accomplish this task. Furthermore, we explored and studied multiple CNN architectures. We show that our method can approximate the similarity between FP patches more efficiently and accurately than the state-of- the-art feature descriptors, including SIFT and SURF using a publicly available dataset. Finally, we observe that our approach, which is purely data-driven, learns that features such as vessels calibre and orientation are important discriminative factors, which resembles the way how humans reason about similarity. To the best of authors knowledge, this is the first attempt to approximate a visual similarity mapping in FP. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Additional Information:SPIE 10133, Medical Imaging 2017: Image Processing, 101332A
Keywords:Neural networks, Deep learning, Medical diagnostics, Image Analysis
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:26901
Deposited On:07 Apr 2017 13:35

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