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Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification

  • Published:2021 Issue: Volume: Page:1-1
  • ISSN:1051-8215
  • Container-title:IEEE Transactions on Circuits and Systems for Video Technology
  • language:
  • Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Avola Danilo1,Cinque Luigi1,Fagioli Alessio2,Filetti Sebastiano3,Grani Giorgio3,Rodola Emanuele1

Affiliation:

1. Department of Computer Science, Sapienza University, Via Salaria 113, 00198, Rome, Italy.

2. Department of Computer Science, Sapienza University, Via Salaria 113, 00198, Rome, Italy. (e-mail: fagioli@di.uniroma1.it)

3. Department of Translational and Precision Medicine, Policlinico Umberto I, Sapienza University, Viale Regina Elena 328, 00161, Rome, Italy.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Thyroid Nodule Detection Using L1‐Norm Inception Deep Neural Network;Integrating Metaheuristics in Computer Vision for Real‐World Optimization Problems;2024-07-31

2. A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization1;Journal of X-Ray Science and Technology;2024-05-28

3. Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules;International Journal for Numerical Methods in Biomedical Engineering;2024-05-12

4. MV-MS-FETE: Multi-view multi-scale feature extractor and transformer encoder for stenosis recognition in echocardiograms;Computer Methods and Programs in Biomedicine;2024-03

5. Thyroid Nodule Detection Using Deep Learning Strategies;2024 International Conference on Emerging Systems and Intelligent Computing (ESIC);2024-02-09
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