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Found 6 papers, 4 papers with code
Masked Image Modelling for retinal OCT understanding
To this end, we leverage Masked Autoencoders (MAE), a simple and scalable method for self-supervised learning, to obtain a powerful and general representation for OCT images by training on 700K OCT images from 41K patients collected under real world clinical settings.
Stochastic Segmentation with Conditional Categorical Diffusion Models
Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving.
Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation
We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Effective semantic segmentation in Cataract Surgery: What matters most?
Our work proposes neural network design choices that set the state-of-the-art on a challenging public benchmark on cataract surgery, CaDIS.
Ranked #1 on
2D Semantic Segmentation task 1 (8 classes)
on CaDIS
2D Semantic Segmentation task 1 (8 classes)
2D Semantic Segmentation task 2 (17 classes)
+2
Deep iterative vessel segmentation in OCT angiography
This paper addresses retinal vessel segmentation on optical coherence tomography angiography (OCT-A) images of the human retina.
