2021
DOI: 10.1002/mp.15049
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Unraveling the interplay of image formation, data representation and learning in CT‐based COPD phenotyping automation: The need for a meta‐strategy

Abstract: Purpose In the literature on automated phenotyping of chronic obstructive pulmonary disease (COPD), there is a multitude of isolated classical machine learning and deep learning techniques, mostly investigating individual phenotypes, with small study cohorts and heterogeneous meta‐parameters, e.g., different scan protocols or segmented regions. The objective is to compare the impact of different experimental setups, i.e., varying meta‐parameters related to image formation and data representation, with the impa… Show more

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Cited by 4 publications
(3 citation statements)
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“…Such feature‐based ML approaches can occasionally outperform DL. [ 79 ] In the optimization of SEMPAI, the added value of the priors for the learning process was evaluated. If models with the hypothesis‐driven priors were superior to models without, or if a prior‐only model shows the same performance as the best DL model, the hypothesis that the prior describes the state of the label well can be considered true.…”
Section: Methodsmentioning
confidence: 99%
“…Such feature‐based ML approaches can occasionally outperform DL. [ 79 ] In the optimization of SEMPAI, the added value of the priors for the learning process was evaluated. If models with the hypothesis‐driven priors were superior to models without, or if a prior‐only model shows the same performance as the best DL model, the hypothesis that the prior describes the state of the label well can be considered true.…”
Section: Methodsmentioning
confidence: 99%
“…Machine Learning Algorithm [12,13]. Use the data classification standard to comprehensively and accurately summarize and sort them, save them in a specific image data folder, form backup data in the background, and save them in the system database to 2 Wireless Communications and Mobile Computing prevent the loss of system data caused by accidents [14].…”
Section: Mural Digital Image Restoration Based Onmentioning
confidence: 99%
“…Finally, the use of priors with AutoML, i.e., without images and DL, is defined as the "maximum" of our prior integration scale. Such feature-based ML approaches can occasionally outperform DL 73 . In the optimization of SEMPAI, the added value of the priors for the learning process is evaluated.…”
Section: Rationale For Standardization and Configuration Spacementioning
confidence: 99%