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Works386 from public data
- Simple online and realtime tracking4,298
Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers.
- DeepFruits: A Fruit Detection System Using Deep Neural Networks1,104
A novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper.
- Generative OpenMax for Multi-Class Open Set Classification479
The proposed method, called G-OpenMax, extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis and provides a way to visualize samples representing the unknown classes from open space.
- Mutual consistency learning for semi-supervised medical image segmentation457
A novel mutual consistency network (MC-Net+) is proposed to effectively exploit the unlabeled data for semi-supervised medical image segmentation to force the model to generate invariant results in such challenging regions, aiming at regularizing the model training.
- Hierarchical Neural Architecture Search for Deep Stereo Matching438
This paper proposes the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework and optimizing the architectures of the entire pipeline jointly.
- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training363
A novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images that consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoder are transformed as an unsupervised loss by a designed cycled pseudo label scheme to encourage mutual consistency.
- 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.
- Medical visual question answering: A survey245
This survey collects and discusses the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature, and summarizes the approaches used in medical V QA tasks.
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- An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP168
A novel interpretable machine learning-based approach termed XG-m7G for the differentiation of m7G sites using the XGBoost algorithm and six different types of sequence-encoding schemes is developed.
- Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases157
This diagnostic study develops and prospectively validates a deep learning algorithm that uses ocular fundus images to recognize numerous retinal diseases in a clinical setting at 65 screening centers in 19 Chinese provinces.
- Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study144
The authors' DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care.
- Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images144
It is demonstrated that the proposed multi-modality method significantly outperforms single- modality methods on three tasks; differentiation between 15 various skin diseases, distinguishing cancerous from non-cancerous moles, and detecting melanoma from benign cases.
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- Vision-Language Navigation with Random Environmental Mixup117
The experimental results on benchmark datasets demonstrate that the augmentation data via REM help the agent reduce its performance gap between the seen and unseen environment and improve the overall performance, making the model the best existing approach on the standard VLN benchmark.
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- Implicit Motion Handling for Video Camouflaged Object Detection111
A new video camouflaged object detection framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames is proposed and unifies motion estimation and object segmentation within a single optimization framework.
- A multimodal vision foundation model for clinical dermatology104
PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities, is shown to have strong performance on the full spectrum of clinically relevant tasks and to increase user accuracy in diagnostic tasks.
- EEG datasets for seizure detection and prediction— A review102
This study compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms, and identified 17 characteristics that make the EEG datasets unique from each other.
- Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy99
A deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information and may potentially assist clinicians in selecting the right drug at the first trial.
- Healthy dietary patterns and the risk of individual chronic diseases in community-dwelling adults97
Using the UK Biobank, associations of four individual healthy dietary scores with the risk of 48 individual chronic diseases are examined to support dietary guidelines for the prevention of most chronic diseases.
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 2026-10-13. 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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