Automated Multi-View Multi-Modal Assessment of Covid-19 Patients Using Reciprocal Attention and Biomedical Transform
Bibliographic Data
| ID | 22079141 |
|---|---|
| Authors | Yanhan Li (0009-0007-4294-1588, Wuhan University), Hongyun Zhao (0000-0002-1251-3888, Dalian Medical University), Tian Gan (0009-0003-8462-7539, Wuhan University), Yang Liu (0000-0003-1220-7044, Wuhan University), Lian Zou (0009-0009-2985-3123, Wuhan University), Ting Xu (0000-0001-5392-4731, Wuhan University), Xuan Chen (0009-0000-2824-1437, BGI Group (China)), Cien Fan (0000-0002-4973-6444, Wuhan University, corresponding author), Meng Wu (0000-0001-8504-6398, Wuhan University, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 886958-886958 |
| Publication date | 2022-05-25 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.886958 |
| PMID | 35692335 |
| OpenAlex | W4281481577 |
| Language | EN |
| References cited | 33 |
Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model
Data mining · Disease · Machine learning · Modal · Pathology · Precision and recall · Recall · Reciprocal · AI in cancer detection · Computer Science · COVID-19 diagnosis using AI · Medicine · Phonocardiography and Auscultation Techniques · Artificial Intelligence
| Highly cited | No |
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