Optic Disc Segmentation

9 papers with code • 7 benchmarks • 4 datasets

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Most implemented papers

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Guzaiwang/CE-Net 7 Mar 2019

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 5 Sep 2016

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.

Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation

utkuozbulak/adaptive-segmentation-mask-attack 30 Jul 2019

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 20 May 2021

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 9 Jul 2021

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 13 Sep 2021

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

reafly/semimedseg 12 Jan 2023

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 8 Jun 2023

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.