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Deep learning in digital pathology image analysis: a survey
deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches...
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| Published in: | Frontiers of medicine 2020-08, Vol.14 (4), p.470-487 |
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| Main Authors: | , , , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c421t-8b774c9012fd2adb4a1ddff1f0c7395a55941f0cce483f6273a70d16d7dce5433 |
| container_end_page | 487 |
| container_issue | 4 |
| container_start_page | 470 |
| container_title | Frontiers of medicine |
| container_volume | 14 |
| creator | Deng, Shujian Zhang, Xin Yan, Wen Chang, Eric I-Chao Fan, Yubo Lai, Maode Xu, Yan |
| description | deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis. |
| doi_str_mv | 10.1007/s11684-020-0782-9 |
| format | article |
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Med</stitle><addtitle>Front Med</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>14</volume><issue>4</issue><spage>470</spage><epage>487</epage><pages>470-487</pages><issn>2095-0217</issn><eissn>2095-0225</eissn><notes>deep learning</notes><notes>detection</notes><notes>pathology</notes><notes>Document received on :2019-08-19</notes><notes>segmentation</notes><notes>classification</notes><notes>Document accepted on :2020-03-05</notes><notes>ObjectType-Article-2</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-3</notes><notes>content type line 23</notes><notes>ObjectType-Review-1</notes><abstract>deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. 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| ispartof | Frontiers of medicine, 2020-08, Vol.14 (4), p.470-487 |
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| subjects | classification Deep Learning detection Histopathology Machine Learning Medical imaging Medicine Medicine & Public Health Pathology Review segmentation Surveys and Questionnaires |
| title | Deep learning in digital pathology image analysis: a survey |
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