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ID 116730
Title Alternative
DRMS for Patient-Level Lymph Node Status Classification
Author
Wang, Lu Tokushima University
Jiang, Xiantao Shanghai Maritime University
Leu, Jenq-Shiou National Taiwan University of Science and Technology
Keywords
Breast cancer metastases
histological lymph node sections
patient level analysis
Content Type
Journal Article
Description
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.
Journal Title
IEEE Access
ISSN
21693536
Publisher
IEEE
Volume
9
Start Page
129293
End Page
129302
Published Date
2021-09-15
Rights
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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DOI (Published Version)
URL ( Publisher's Version )
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language
eng
TextVersion
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departments
Science and Technology