The phrase "a trained image" is correct and usable in written English.
It can be used in contexts related to machine learning, computer vision, or photography, where an image has been processed or modified through training algorithms or techniques.
Example: "The model was able to accurately classify objects in the scene due to the use of a trained image."
Alternatives: "an optimized image" or "a processed image."
The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist).
Brain infarcts were assessed by a trained image analyst (G.M.P).
Stroke lesions were visually identified, by a trained image analyst, as being hypointense compared to homologous contralateral tissue.
All lesions were traced by a trained image analyst and confirmed by an experienced clinical neurologist, who was blind to all clinical data, except for the side of hemiparesis.
Hippocampi were manually traced bilaterally by a trained image analyst, who was blinded to all demographic variables, and had established excellent reliability with 'gold standard' ratings on a training set of six pairs of hippocampi (intraclass correlation coefficient ∼0.90).
A trained image analyst (6 months of experience with MRE) in the MR3T research laboratory manually placed 2-cm-diameter circular ROIs in each of the nine hepatic segments on the maps, avoiding large blood vessels, liver edges, and (if any) artifacts.
The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization.
This study showed how analogy of modern occurrences can be set as the foundation for generating a training image.
In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image.
Multiple-point (geo)statistics (MPS) is a tool that gives the opportunity of reaching this goal via defining a training image (TI).
In this paper, a sequential compressive sampling method is presented which decides the location of new samples based on a training image.
