Name
Title | ||
|---|---|---|
A Survey For Breast Histopathology Image Analysis Using Classical And Deep Neural Networks |
Abstract | ||
|---|---|---|
Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-23762-2_20 | INFORMATION TECHNOLOGY IN BIOMEDICINE |
Keywords | DocType | Volume |
Breast cancer, Histopathology image, Artificial neural networks, Deep learning, Feature extraction, Classification | Conference | 1011 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Li | 1 | 4 | 5.18 |
| Dan Xue | 2 | 0 | 1.01 |
| Zhijie Hu | 3 | 0 | 1.01 |
| hao chen | 4 | 8 | 1.61 |
| Yudong Yao | 5 | 0 | 0.68 |
| Yong Zhang | 6 | 0 | 0.34 |
| Mo Li | 7 | 6 | 3.63 |
| Qian Wang | 8 | 245 | 55.19 |
| Ning Xu | 9 | 0 | 1.35 |