Name
Abstract | ||
|---|---|---|
Mitotic counts are widely used as a metric for cellular proliferation for prognosis and to determine the aggressiveness of individual cancers. This study presents a less labor-intensive method to count mitotic cells in breast cell sections. The proposed algorithm involves two phases: candidate segmentation and detection. During candidate segmentation, images are filtered through a blue ratio threshold to remove unnecessary background information and to increase the color difference between targets and non-targets for an entire digitized image. A fuzzy candidate segmentation method is used to adaptively determine threshold values in order to dichotomize gray-level images and distinguish the images of mitotic candidates from the background. The thresholding scheme integrates the spatial characteristics’ distribution in a histogram to determine an intensity threshold for the processed image, in order to filter insignificant information. During the detection phase, a two-class classification uses an attention mechanism that is realized by a set of fully connected neural networks, instead of convolutional layers, which decreases the computational cost. The validation test using ICPR2012 competition datasets shows that the proposed model outperforms current state-of-art techniques, in terms of the metrics, Accuracy, F1-score, and Precision and Recall. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s40815-020-00868-z | International Journal of Fuzzy Systems |
Keywords | DocType | Volume |
Attention mechanism, Recurrent neural network, Image segmentation, Mitosis detection of breast cancer | Journal | 22 |
Issue | ISSN | Citations |
5 | 1562-2479 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maxwell Hwang | 1 | 2 | 1.72 |
| Da Wang | 2 | 1 | 0.70 |
| Cai Wu | 3 | 1 | 0.36 |
| Wei-Cheng Jiang | 4 | 33 | 9.51 |
| Xiang-Xing Kong | 5 | 1 | 0.70 |
| kaoshing hwang | 6 | 399 | 59.91 |
| Ke-Feng Ding | 7 | 3 | 3.43 |