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Claim this profileWorks108 from public data
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- Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding113
Hulu-Med is introduced, a transparent, generalist medical Vision-Language Model (VLM) designed to unify language-only, 2D/3D vision-language, and video understanding within a single architecture, and outperforms proprietary systems such as GPT-4o on 16 benchmarks.
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- Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework80
This is the first framework that utilizes a hybrid classification framework for the detection task and integrates the extracting region-of-interest (ROI) component based on target detection, retaining a temporal dependency module and classification module based on a domain-transferred deep convolutional neural network (CNN).
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- MLMSeg: A multi-view learning model for ultrasound thyroid nodule segmentation52
The exceptional segmentation capability of MLMSeg for thyroid nodules can greatly assist in localizing thyroid nodules and facilitating more precise measurements of their transverse and longitudinal diameters, which is of significant clinical relevance for the diagnosis of thyroid nodules.
- HN-PPISP: a hybrid network based on MLP-Mixer for protein–protein interaction site prediction46
A novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer module for local feature extraction and a two-stage multi-branch module for global feature capture and merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction.
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- A knowledge-interpretable multi-task learning framework for automated thyroid nodule diagnosis in ultrasound videos34
This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, by simulating the typical diagnostic workflow used by radiologists and demonstrated this approach outperformed other state-of-the-art video classification methods.
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- HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system28
A fuzzy function is designed to address the uncertainty of task’s economic profits and execution time and some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment.
- ED-ACNN: Novel attention convolutional neural network based on encoder–decoder framework for human traffic prediction27
It is concluded that the c l o s e n e s s feature is the most significant for human traffic prediction in the proposed approach, and the results show that the proposed method can be very competitive with state-of-the-art baselines.
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- CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos21
This work proposes a novel reconstruction-based approach named CardiacNet to learn a better representation of local cardiac structures and motion abnormalities through echocardiogram videos and proposes benchmark datasets named CardiacNet-PAH and CardiacNet-ASD to evaluate the effectiveness of cardiac disease assessment.
- Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support20
An ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost, which reaches better classification performance when compared with existing methods and can potentially be enhanced and supported in medical tasks.
- DH-GAC: deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation19
Experimental results demonstrate that DH-GAC not only improves the segmentation precision, but also overcomes the intrinsic drawback of classical geodesic active contour in boundary delineation.
- Deep endpoints focusing network under geometric constraints for end-to-end biometric measurement in fetal ultrasound images18
This is the first AI-based application to address the biometric measurement of irregular anatomical structures in fetal ultrasound images with an end-to-end approach and experiment results showed that E2EBM-Net outperformed the existing methods and achieved the state-of-the-art performance.
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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 2026-10-12. 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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