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- Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs304
Application of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON, suggesting that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.
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- Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model274
The proposed AG-CNN approach significantly advances state-of-the-art glaucoma detection, including an attention prediction subnet, a pathological area localization subnet and a glauca classification subnet.
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- DeepVS: A Deep Learning Based Video Saliency Prediction Approach191
A large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects’ fixations on 538 videos, is established and an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets.
- Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis78
Experimental results show that the joint learning framework in the DeepSC-COVID model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.
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- Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images73
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- Deep Multi-Task Learning for Diabetic Retinopathy Grading in Fundus Images55
A deep multi-task learning based DR grading (DeepMT-DR) method for LR fundus images, which simultaneously handles the auxiliary tasks of ISR and lesion segmentation and the high-level task of DR grading.
- Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching54
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios and a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation.
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- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
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- Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method25
A novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains, including two main stages: the complex dense encoder and three-stream multi-domain decoder.
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- A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging23
A transformer-based deep learning system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics is developed and the nephrologists’ diagnosis accuracy can be improved by 21%, through visualization assistance of the DL system.
- Bottom-up saliency detection with sparse representation of learnt texture atoms22
It is validated that the proposed SR-LTA feature for bottom-up saliency detection, in light of the learnt salient and non-salient dictionaries, can advance state-of-the-art saliency Detection on natural images.
- DeepVS2.0: A Saliency-Structured Deep Learning Method for Predicting Dynamic Visual Attention20
A novel DNN-based video saliency prediction method, called DeepVS2.0, is proposed, which establishes a large-scale eye-tracking database of videos (LEDOV) and develops a saliency-structured convolutional long short-term memory network (SS-ConvLSTM), to consider the center-bias and sparsity of human attention maps.
- Progressive Training of A Two-Stage Framework for Video Restoration16
This work proposed a two-stage framework including a multi-frame recurrent network and a single-frame transformer to cope with the training of recurrent neural networks and transformers, which wins two champions and a runner-up in the NTIRE 2022 super-resolution and quality enhancement of compressed video challenges.
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 2026-10-15. 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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