Name
Title | ||
|---|---|---|
Fully-Channel Regional Attention Network For Disease-Location Recognition With Tongue Images |
Abstract | ||
|---|---|---|
Objective: Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model. Methods: To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism. Results: The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition. Conclusion: In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency. Significance: The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.artmed.2021.102110 | ARTIFICIAL INTELLIGENCE IN MEDICINE |
Keywords | DocType | Volume |
Tongue image modeling, Disease-position recognition, Convolutional networks, Regional detailed features, Attention mechanism | Journal | 118 |
ISSN | Citations | PageRank |
0933-3657 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Hu | 1 | 10 | 3.61 |
| Guihua Wen | 2 | 16 | 8.69 |
| Mingnan Luo | 3 | 0 | 0.34 |
| Pei Yang | 4 | 58 | 17.32 |
| Dan Dai | 5 | 3 | 3.40 |
| Zhiwen Yu | 6 | 65 | 10.06 |
| Changjun Wang | 7 | 1 | 1.02 |
| Hall Wendy | 8 | 0 | 0.68 |