Optic Disc Segmentation
10 papers with code • 8 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Optic Disc Segmentation
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STARE
REFUGE Challenge
ADAM
MESSIDOR
SMDG
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Guzaiwang/CE-Net
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In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Deep Retinal Image Understanding
PB17151764/2020UM-Summer-Research
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This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Segmentation is a fundamental task in medical image analysis.
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
utkuozbulak/adaptive-segmentation-mask-attack
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Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models.
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
askerlee/segtran
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Medical image segmentation is important for computer-aided diagnosis.
U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
bionicvisionlab/2021-HBA-U-Net
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The network consists of a novel bottleneck attention block that combines and refines self-attention, channel attention, and relative-position attention to highlight retinal abnormalities that may be important for fovea and OD segmentation in the degenerated retina.
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
ShishuaiHu/DCAC
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In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain.
Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation
Since the two tasks rely on similar feature information, the unlabeled data effectively enhances the representation of the network to the lesion regions and further improves the segmentation performance.
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
shishuaihu/ccsdg
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In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations.
