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. 2020 Jul 31;2(4):e190047. doi: 10.1148/rycan.2020190047

Figure 2:

Texture or radiomic feature extraction: handcrafted feature versus deep features. During the process of texture or radiomic analysis, quantitative imaging features are extracted with the potential to serve as quantitative biomarkers that can be used to predict a clinical or molecular end point of interest. Broadly, traditional radiomic features may be defined as those derived using clearly defined or explicit mathematical formulas designed by experts, often independently and prior to the experiment, which may in turn be referred to as handcrafted or hand-engineered features. In contradistinction, features extracted based on image analysis with deep learning approaches, such as convolutional neural networks, are not clearly definable or derived using expert-designed explicit mathematical formulas. Instead, they are learned from data through a learning algorithm. These may be referred to as deep (extracted) features and the process as “deep radiomics.”

Texture or radiomic feature extraction: handcrafted feature versus deep features. During the process of texture or radiomic analysis, quantitative imaging features are extracted with the potential to serve as quantitative biomarkers that can be used to predict a clinical or molecular end point of interest. Broadly, traditional radiomic features may be defined as those derived using clearly defined or explicit mathematical formulas designed by experts, often independently and prior to the experiment, which may in turn be referred to as handcrafted or hand-engineered features. In contradistinction, features extracted based on image analysis with deep learning approaches, such as convolutional neural networks, are not clearly definable or derived using expert-designed explicit mathematical formulas. Instead, they are learned from data through a learning algorithm. These may be referred to as deep (extracted) features and the process as “deep radiomics.”