Papers Optic Disc Segmentation
“Optic Disc Segmentation” 태그가 달린 논문 19편 · 필터 해제
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generaliza…
DisentanglementDomain AdaptationDomain GeneralizationImage Segmentation+5Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation
Biomedical image segmentation plays a significant role in computer-aided diagnosis. However, existing CNN based methods rely heavily on massive manual annotations, which are very expensive and require huge human resource…
Image SegmentationMedical Image SegmentationOptic Disc SegmentationSegmentation+1REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of …
Domain AdaptationOptic Disc SegmentationOptic Disc Segmentation using Disk-Centered Patch Augmentation
The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a …
DiagnosticOptic Disc SegmentationDomain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this…
DecoderDomain GeneralizationImage SegmentationLesion Segmentation+4U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practi…
Fovea DetectionOptic Disc DetectionOptic Disc SegmentationSegmentationMedical Image Segmentation Using Squeeze-and-Expansion Transformers
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large conte…
Brain Tumor SegmentationDomain GeneralizationImage SegmentationInductive Bias+6Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder
The accurate detection of retinal structures like an optic disc (OD), cup, and fovea is crucial for the analysis of Age-related Macular Degeneration (AMD), Glaucoma, and other retinal conditions. Most segmentation method…
Fovea DetectionOptic Cup DetectionOptic Cup SegmentationOptic Disc Detection+2Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images
Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based…
DiversityOptic Disc SegmentationSegmentationTransfer LearningFundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report
Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for compute…
Fovea DetectionOptic Disc SegmentationSegmentationAutomatic lesion segmentation and Pathological Myopia classification in fundus images
In this paper we present algorithms to diagnosis Pathological Myopia (PM) and detection of retinal structures and lesions such asOptic Disc (OD), Fovea, Atrophy and Detachment. All these tasks were performed in fundus im…
ClassificationGeneral ClassificationLesion SegmentationOptic Disc Segmentation+1The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detection
The optic disc segmentation is an important step for retinal image-based disease diagnosis such as glaucoma. The inner limiting membrane (ILM) is the first boundary in the OCT, which can help to extract the retinal pigme…
DecoderOptic Disc SegmentationSegmentationImpact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully cra…
Image SegmentationLesion SegmentationMedical Image AnalysisOptic Disc Segmentation+3ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obtaining accurate segmentation. In this pa…
Image SegmentationMedical Image AnalysisMedical Image SegmentationOptic Disc Segmentation+2CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such …
Cell SegmentationDecoderImage SegmentationMedical Image Analysis+5Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods to medical images is the lack of labeled…
Image SegmentationLesion SegmentationLiver SegmentationMedical Image Segmentation+6Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminato…
Generative Adversarial NetworkGPUImage SegmentationOptic Disc Segmentation+2Deep Retinal Image Understanding
This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks …
General Classificationimage-classificationImage Classificationobject-detection+3U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmenta…
Cell SegmentationCell TrackingColorectal Gland Segmentation:Crack Segmentation+14