A scalable hardware architecture for retinal blood vessel detection in high resolution fundus images

Bendaoudi, Hamza; Cheriet, Foued; Tahar, Houssem Ben; Langlois, J. M. Pierre

doi:10.1109/dasip.2014.7115619

Cited by 2 publications

(1 citation statement)

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“…In some cases, the general-purpose GPU (GPGPU) option might not be able to provide the energy efficiency needed for the required computation performance. In this case, field-programmable gate arrays (FPGAs) 169 may be used as an alternative to embedded GPU, as demonstrated for retinal image analysis 170 and real-time video restoration. 171 FPGA implementation may, however, be problematic due to increased implementation complexity.…”

Section: Distributing the Computational Loadmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

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Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for ‘active acquisition’–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via ‘active acquisition’ serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining.

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Section: Distributing the Computational Loadmentioning

confidence: 99%