arXiv Machine Learning By Michael Chertok, Alon Tiosano, Orly Gal-Or, Lior Kramarski, Einav Baharav Shlezinger, Irit Bahar, Lior Wolf

In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis

Read the original on arXiv Machine Learning →

arXiv:2608. 15626v1 Announce Type: new Abstract: Optical coherence tomography angiography (OCTA) images retinal blood flow, giving capillary-perfusion and foveal-avascular-zone biomarkers that grade diabetic-retinopathy ischemia.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
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DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation

arXiv:2607. 29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated data in medical imaging, particularly in optical coherence tomography (OCT) of mouse eyes, where manual retinal layer delineation is labour-intensive due to tiny structures and required expertise, resulting in scarce datasets.

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EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining

arXiv:2606. 15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information.

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