35 citations · 80 across the 10 of their papers we have counts for
10 papers
Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis
Lovre Antonio Budimir, Mingya Alexa Gong, Alyssa Foong Quinney +5
Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently adapted across diverse imaging…
Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Mingya Alexa Gong, Da Ma, Lovre Antonio Budimir +7
Despite the widespread adoption of foundation models as feature extractors for medical imaging, relatively little is understood about how different pretraining strategies influence…
Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning
Timothy T. Yu, Da Ma, Jayden Cole +3
Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments t…
Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
Shuo Chen, Da Ma, Sieun Lee +7
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imagin…
Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography
Ricky Chen, Timothy T. Yu, Gavin Xu +3
With the FDA approval of Artificial Intelligence (AI) for point-of-care clinical diagnoses, model generalizability is of the utmost importance as clinical decision-making must be d…
Pseudo-real-time retinal layer segmentation for high-resolution adaptive optics optical coherence tomography
Worawee Janpongsri, Joey Huang, Ringo Ng +3
We present a pseudo-real-time retinal layer segmentation for high-resolution Sensorless Adaptive Optics-Optical Coherence Tomography (SAO-OCT). Our pseudo-real-time segmentation me…
Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning
Julian Lo, Morgan Heisler, Vinicius Vanzan +6
Purpose: Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complica…
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Donghuan Lu, Morgan Heisler, Da Ma +5
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is wid…
Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms
Morgan Heisler, Forson Chan, Zaid Mammo +15
Automatic quantification of perifoveal vessel densities in optical coherence tomography angiography (OCT-A) images face challenges such as variable intra- and inter-image signal to…
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
Donghuan Lu, Morgan Heisler, Sieun Lee +3
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. Therefore it is commonly used in the diagn…