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2019
State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts.
Gil, Debora +4 more
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State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts.
Gil, Debora +4 more
openaire +1 more source
An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly
Innovations in research and clinical care using patient‐generated health data
Ca-A Cancer Journal for Clinicians, 2020H S L Jim +2 more
exaly
Integrated data could augment resilience
Science, 2019Farshid, Vahedifard +3 more
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The Data Augmentation Algorithm
1991Analogous to the EM algorithm, the data augmentation algorithm exploits the simplicity of the likelihood function or posterior distribution of the parameter given the augmented data. In contrast to the EM algorithm, the present goal is to obtain the entire (normalized) likelihood or posterior distribution, not just the maximizer and the curvature at ...
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