| Item type |
文献 / Documents(1) |
| 公開日 |
2022-03-15 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1109/ACCESS.2021.3113036 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1109/ACCESS.2021.3113036 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Deep Regional Metastases Segmentation for Patient-Level Lymph Node Status Classification |
|
言語 |
en |
| タイトル別表記 |
|
|
その他のタイトル |
DRMS for Patient-Level Lymph Node Status Classification |
|
言語 |
en |
| 著者 |
Wang, Lu
宋, 天
片山, 貴文
Jiang, Xiantao
島本, 隆
Leu, Jenq-Shiou
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Generally, automatic diagnosis of the presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Detection and classification of breast cancer metastases have high clinical relevance, especially in whole-slide images of histological lymph node sections. Fast early detection leads to huge improvement of patient’s survival rate. However, currently pathologists mainly detect the metastases with microscopic assessments. This diagnosis procedure is extremely laborious and prone to inevitable missed diagnoses. Therefore, automated, accurate patient-level classification would hold great promise to reduce the pathologist’s workload while also reduce the subjectivity of diagnosis. In this paper, we provide a novel deep regional metastases segmentation (DRMS) framework for the patient-level lymph node status classification. First, a deep segmentation network (DSNet) is proposed to detect the regional metastases in patch-level. Then, we adopt the density-based spatial clustering of applications with noise (DBSCAN) to predict the whole metastases from individual slides. Finally, we determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection in clinical trials. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Breast cancer metastases |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
histological lymph node sections |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
patient level analysis |
| 書誌情報 |
en : IEEE Access
巻 9,
p. 129293-129302,
発行日 2021-09-15
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| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
21693536 |
| 出版者 |
|
|
出版者 |
IEEE |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| EID |
|
|
識別子 |
382172 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
access_9_129293.pdf (1.47 MB)
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