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Works482 from public data
- Deep Learning in Medical Ultrasound Analysis: A Review789
Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.
- FAT-Net: Feature adaptive transformers for automated skin lesion segmentation594
A novel feature adaptive transformer network based on the classical encoder-decoder architecture, named FAT-Net, which integrates an extra transformer branch to effectively capture long-range dependencies and global context information is proposed.
- Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM286
This work designs a novel deep learning framework and exploits the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) to derive deep feature representation from both MRI and PET for AD diagnosis.
- Skin lesion segmentation via generative adversarial networks with dual discriminators279
The proposed GAN achieves superior segmentation performance to state-of-the-art methods and is evaluated on the public International Skin Imaging Collaboration (ISIC) Skin Lesion Challenge Datasets of 2017 and 2018.
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- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review268
Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
- SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation259
The experimental results demonstrate the effectiveness of the proposed SCS-Net, which is capable of achieving better segmentation performance than other state-of-the-art approaches, especially for the challenging cases with large scale variations and complex context environments.
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- Blind and robust audio watermarking scheme based on SVD–DCT193
Experimental results show that the proposed watermarking method is comparable to, if not, better than SVD based method and several selected typical audioWatermarking methods, even in the presence of various common signal processing attacks.
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- A Robust Audio Watermarking Scheme Based on Lifting Wavelet Transform and Singular Value Decomposition161
Experimental and analysis results demonstrate that the proposed LWT-SVD method is not only robust against both general signal processing attacks and desynchronization attacks but also achieve a very good tradeoff between robustness, imperceptibility and payload.
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- Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis147
A joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis and demonstrates that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.
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- Fully transformer network for skin lesion analysis141
This work proposes a Fully Transformer Network (FTN) to learn long-range contextual information for skin lesion analysis and shows that FTN consistently outperforms other state-of-the-art CNNs in terms of computational efficiency and the number of tunable parameters.
- Reversible watermarking scheme for medical image based on differential evolution141
Experimental results indicate that the proposed reversible watermarking method is not only highly competitive, but also outperforms the existing methods.
- Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment139
The proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis and achieves superior performance compared with existing methods.
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- Morphological Feature Visualization of Alzheimer’s Disease via Multidirectional Perception GAN132
By introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently and clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain.
- Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction129
This paper proposes three mechanisms to improve GCN, namely similarity-aware adaptive calibrated GCN (SAC-GCN), for predicting SMC and MCI, and devise a calibration mechanism to fuse functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) information into edges.
- Multi-task learning for quality assessment of fetal head ultrasound images128
A new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment and achieves promising performance compared with state-of-the-art methods.
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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