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Chapter and Conference Paper
Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT: A PINNACLE Study Report
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, bo...
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Chapter and Conference Paper
TINC: Temporally Informed Non-contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contr...
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Chapter and Conference Paper
Construction of a Retinal Atlas for Macular OCT Volumes
Optical Coherence Tomography (OCT) plays an important role in the analysis of retinal diseases such as Age-Related Macular Degeneration (AMD). In this paper, we present a method to construct a normative atlas ...
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Chapter and Conference Paper
End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Coherence Tomography
Intra-retinal layer segmentation of Optical Coherence Tomography images is critical in the assessment of ocular diseases. Existing Energy minimization based methods employ handcrafted cost terms to define thei...
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Chapter and Conference Paper
Coupled Sparse Dictionary for Depth-Based Cup Segmentation from Single Color Fundus Image
We present a novel framework for depth based optic cup boundary extraction from a single 2D color fundus photograph per eye. Multiple depth estimates from shading, color and texture gradients in the image are cor...
