Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks162 from public data
- 313
- Cervical cell classification with graph convolutional network129
A novel cervical cell classification method based on Graph Convolutional Network (GCN) that can achieve the better classification performance and also can be potentially used in automatic screening system of cervical cytology.
- DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system117
Two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation are released, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
- Super-resolution reconstruction of MR image with a novel residual learning network algorithm113
This work proposes a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL), which works effectively in capturing high-frequency details by learning local residuals.
- Adaptive color deconvolution for histological WSI normalization109
The proposed novel adaptive color deconvolution (ACD) algorithm can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.
- 95
- Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks89
This paper proposes a Sparse Sampling Transformer based on Uncertainty-Driven Ranking, dubbed UDR-S2Former, which can adaptively sample relevant image degradation information to model underlying degradation relationships and clearly outperforms state-of-the-art methods for all benchmarks.
- 77
- 64
- AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results63
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
- 60
- Graph Convolutional Networks for Cervical Cell Classification59
This paper presents a novel cervical cell classification method based on Graph Convolutional Network (GCN), which uses GCN to explore the image-level potential relationship for improving the classification performance.
- 56
- Remote sensing scene classification based on high-order graph convolutional network51
This paper presents a novel remote sensing scene classification method based on high-order graph convolutional network (H-GCN), which uses the attention mechanism to focus on the key components inside the image during CNN feature learning and high- order graph convolutionsal network is applied to investigate the class dependencies.
- Sparsity-guided saliency detection for remote sensing images50
A sparsity-guided saliency detection model for remote sensing images that uses a sparse representation to obtain the high- level global and background cues for saliency map integration and a Bayesian formula is applied to integrate the saliency maps generated by both types of high-level cues.
- 40
- 38
- 37
- Relevance-Aware Anomalous Users Detection in Social Network via Graph Neural Network36
An innovate Relevance-aware Anomalous Users Detection model (RAUD) is proposed, which extracts and learns both the explicit and implicit relevance from meta data based on heterogeneous information network (HIN) and GNNs.
- 32
- Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval29
A novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques is proposed, which ensures the framework is efficient and adequate for practical large-scale W SI database.
- Encoding Histopathological WSIs Using GNN for Scalable Diagnostically Relevant Regions Retrieval27
A novel CBHIR framework for regions retrieval from WSI-database based on hierarchical graph neural networks (GNNs) is proposed, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution.
- Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification26
A novel positional encoding-guided transformer-based multiple instance learning (PEGTB-MIL) method for histopathology WSI classification that utilizes positional encoding to effectively guide and reinforce MIL, leading to enhanced performance on downstream WSI classification tasks.
- Future Event Prediction Based on Temporal Knowledge Graph Embedding26
A new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently is proposed.
- Deep Natural Language Processing for Search and Recommender Systems26
This tutorial first introduces deep learning based NLP technologies, including language understanding and language generation, and details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and languagegeneration.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.