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- CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation293
This work proposes a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography, color fundus image, and corneal confocal microscopy, and instead of the U-Net based convolutional neural network, a novel network which includes a self-attention mechanism in the encoder and decoder.
- Dense Dilated Network With Probability Regularized Walk for Vessel Detection170
The proposed novel method for retinal vessel detection includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection.
- Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images148
This work first extracts the structure of the retinal images, then it combines both the structure features and the last layer features extracted from original health image to reconstruct the original input healthy image, and measures the difference between structure extracted from Original and the reconstructed image.
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- Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading91
A Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures.
- Sparse-Gan: Sparsity-Constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image82
A novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set is proposed and the results show that the proposed method outperforms the state-of-the-art methods.
- SkrGAN: Sketching-Rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis65
Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI).
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- DeepDisc: Optic Disc Segmentation Based on Atrous Convolution and Spatial Pyramid Pooling21
It is demonstrated that the DeepDisc system achieves state-of-the-art disc segmentation performance on the ORIGA and Messidor datasets without any post-processing strategies, such as dense conditional random field.
- Learning Intra-View and Cross-View Geometric Knowledge for Stereo Matching18
This work proposes a novel Intra-view and Cross-view Geometric knowledge learning Network (ICGNet), specifically crafted to assimilate both intra-view and cross-view geo-metric knowledge.
- Fundus Image Quality-Guided Diabetic Retinopathy Grading13
This work proposes Fundus Image Quality (FIQ)-guided DR grading method based on multi-task deep learning, which is the first work using fundus image quality to help grade DR.
- SuperJunction: Learning-Based Junction Detection for Retinal Image Registration10
A novel learning-based junction detection approach for retinal image registration, which enhances both the keypoint detector and descriptor training, and considers the non-linearity between retinal images from different views during matching.
- Automatic Localization of Optic Disc using Modified U-Net7
This paper proposes a method based on U-net and Depth-First-Select Graph to accurately and efficiently locate the optic disc and outperforms other optic disc localization algorithms.
- Structure-preserving guided retinal image filtering for optic disc analysis6
A method to overcome the issue of low image quality due to the disease like cataract is introduced and its application in automatic disc analysis and the method has potential to improve disease detection such as glaucoma.
- Large-Scale Left and Right Eye Classification in Retinal Images6
This work spends a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset, based on the developed online labeling system, to train classification models based on convolutional neural networks to discriminate left andright eyes in fundus images.
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- MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation3
Online feedback sampling is proposed to augment the training samples across different scales, which increases the diversity of training data automatically and achieves the state-of-the-art performance in low computational resources.
- Correction to “Noise Adaptation Generative Adversarial Network for Medical Image Analysis”3
In the above article [1] , Tables II, III, and V and Fig. 6 are incorrect.
- Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning2
Noise-space Hamiltonian Monte Carlo (N-HMC), a posterior sampling method that treats reverse diffusion as a deterministic mapping from initial noise to clean images, is proposed and extended to a noise-adaptive variant (NA-NHMC) that effectively handles IPs with unknown noise type and level.
- SURE: Semi-dense Uncertainty-REfined Feature Matching2
This work proposes SURE, a Semidense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence by modeling both aleatoric and epistemic uncertainties and introduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module that enhances local feature precision with minimal overhead.
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- Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing1
This paper extends neural radiance fields (NeRF) to jointly encode semantics, appearance and geometry to achieve accurate 2D semantic labels using noisy pseudo-label supervision and achieves consistent human parsing in both continuous and novel views.
- The Channel Attention Based Context Encoder Network for Inner Limiting Membrane Detection1
This paper builds a new optic disc centered dataset from 20 volunteers and manually annotated the ILM boundary in each OCT scan as ground-truth and proposes a channel attention based context encoder network modified from the CE-Net to segment the optic disc.
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- Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection–
This paper introduces FADNet (Face Authenticity Detector Net), a self-supervised framework that which reformulates face forgery detection as a one-class classification (OCC) task and substantially outperforms existing state-of-the-art (SOTA) methods.
- Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning–
This work proposes EMSFD (Evidence-based decision Modeling for Synthetic Face Detection with uncertainty-driven active learning), an approach designed to enhance detection reliability and generalizability and yields a 15\% increase in accuracy.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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