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Works468 from public data
- 836
- Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease634
This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities to aid the diagnosis of AD and has the potential to require less labeled data.
- Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization592
A new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments is proposed, which indicates the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
- 523
- 496
- Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis402
This paper provides an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator for aggregating hypothetical curves in point clouds.
- Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data234
This paper presents a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieved, and retrieval from diverse datasets.
- A speculative approach to spatial‐temporal efficiency with multi‐objective optimization in a heterogeneous cloud environment209
An adaptive method aiming at spatial-temporal efficiency in a heterogeneous cloud environment based on an optimized Kernel-based Extreme Learning Machine algorithm is presented for faster forecast of job execution duration and space occupation and achieves 26.6% improvement over the original scheme.
- 192
- NFN＋: A novel network followed network for retinal vessel segmentation190
The proposed NFN＋ model, to the best knowledge, achieved the state-of-the-art retinal vessel segmentation accuracy on color fundus images (AUC: 98.30%, 98.75% and 98.94%, respectively).
- 170
- Multiscale Network Followed Network Model for Retinal Vessel Segmentation169
The proposed multiscale network followed network (MS-NFN) model achieved the state-of-the-art retinal vessel segmentation accuracy on both datasets.
- 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes161
A 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes and inherits the desired strong generalization capability for within- slice information while naturally exploiting between-slice information for more effective modelling is proposed.
- Vessel-Net: Retinal Vessel Segmentation Under Multi-path Supervision155
A novel and lightweight deep learning model called Vessel-Net for retinal vessel segmentation, which designs an efficient inception-residual convolutional block to combine the advantages of the Inception model and residual module for improved feature representation and introduces four supervision paths to preserve the rich and multi-scale deep features during model optimization.
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- DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations139
DeepGene, an advanced cancer type classifier, is proposed, which addresses the obstacles in existing SMCC studies and outperforms three widely adopted existing classifiers, mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.
- 137
- CelebV-Text: A Large-Scale Facial Text-Video Dataset129
The effectiveness and potential of CelebV- Text are shown through extensive self-evaluation, and a benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task.
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- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture108
A novel Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a recurrent manner without introducing any extra parameters that significantly outperforms the vanilla U-Net as well as other state-of-the-art methods.
- Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach107
Experimental results show that the robustness, accuracy and efficiency of the proposed UP method compare favorably to the state-of-the-art one sample based methods.
- A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs106
This study demonstrated that a deep learning-based algorithm can identify glaucomatous discs at high accuracy level using monoscopic fundus images, highlighting the algorithm's potential application in large population-based disease screening or telemedicine programs.
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 2026-10-16. 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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