Name
Title | ||
|---|---|---|
Improvement of Whole-Slide Pathological Image Recognition Method Based on Deep Learning |
Abstract | ||
|---|---|---|
The recognition and classification of whole-slide pathological images is the core technology of computer-aided diagnosis of cancer. This paper proposes a new data set construction method to improve the computer-aided diagnosis method based on deep learning. Taking the pathological image of breast cancer as an example, the image was preprocessed by the otsu algorithm, and the amount of calculation was reduced by removing the blank area caused by tissue slide production. The noise picture database was established, and the data set was established by two different division strategies. The pre-trained googlenet model is used to train it; then the trained model is used to classify and diagnose pathological images. The experimental results show that the model AUC of the improved data set reaches 0.8410. An improved whole-slide pathology image recognition method based on deep learning is expected to be more widely used in clinical practice, making cancer diagnosis more efficient. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISCID.2018.10163 | 2018 11th International Symposium on Computational Intelligence and Design (ISCID) |
Keywords | Field | DocType |
component,Deep learning,Transfer learning,Whole-slide image,Pathological image,Cancer | Computer vision,Pattern recognition,Computer science,Clinical Practice,Transfer of learning,Blank,Artificial intelligence,Deep learning,Construction method | Conference |
Volume | ISSN | ISBN |
02 | 2165-1701 | 978-1-5386-8528-0 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojun Ma | 1 | 0 | 0.34 |
| Haixia Liu | 2 | 9 | 2.98 |
| Yanxiong Niu | 3 | 0 | 2.37 |
| Chengfen Zhang | 4 | 0 | 0.34 |
| Di Liu | 5 | 71 | 21.08 |