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Cui et al., 2020 - Google Patents

A deep learning-based framework for lung cancer survival analysis with biomarker interpretation

Cui et al., 2020

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Document ID
15771085153510234521
Author
Cui L
Li H
Hui W
Chen S
Yang L
Kang Y
Bo Q
Feng J
Publication year
Publication venue
BMC bioinformatics

External Links

Snippet

Background Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and …
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Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06T2207/10024—Color image
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    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
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