Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning from Label Fuzzy Proportions

Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning from Label Fuzzy Proportions

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin...

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Bibliographic Details
Main Authors: Bai, X. (Author), Ge, H. (Author), Li, J. (Author), Liang, T. (Author), Lu, N. (Author), Tian, Y. (Author), Wu, J. (Author), Ye, Q. (Author), Zhang, Q. (Author), Zhou, T. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
accuracy
algorithm
Article
Cell death
Concordance index
concordance index of the slide necrosis score
cross validation
Cross validation
deep learning
Deep learning
Deep Learning
diagnostic imaging
Diseases
ensemble learning
Ensemble learning
feasibility study
fuzzy system
HE staining
histopathological image analysis
Histopathological image analysis
histopathology
human
human tissue
Humans
image processing
Image Processing, Computer-Assisted
Improve performance
Integration models
label fuzzy proportion
learning from label fuzzy proportions
Learning systems
liver cancer
Liver Neoplasms
liver tumor
machine learning
micronecrosis
necrosis
overall survival
pathology
Quantitative method
receiver operating characteristic
scoring system
sensitivity and specificity
staining
tumor micronecrosis quantification
tumor pathological micronecrosis
Tumor progressions
Tumors
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