Name
Abstract | ||
|---|---|---|
Breast cancer is one of the most common cancers worldwide, and early detection can significantly reduce its mortality rate. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. By aggregating features at different scales, MS-GWNN can encode the multi-scale contextual interactions in the whole pathological slide. Experimental results on two public datasets demonstrate the superiority of MS-GWNN. Moreover, ablation studies show that multiscale analysis has a significant impact on the accuracy of cancer diagnosis. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISBI52829.2022.9761464 | 2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022) |
Keywords | DocType | ISSN |
Graph wavelet neural network, breast cancer diagnosis, histopathological image | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mo Zhang | 1 | 0 | 0.68 |
| Bin Dong | 2 | 0 | 0.68 |
| Quanzheng Li | 3 | 0 | 0.34 |