Illustration of the proposed sampling protocol.

<p><i>k</i> random points (green dots) are drawn from the sampling set (blue dots). The subset of sampled data points is defined by the data points that are closest to each drawn point (orange stars). Red squares represent the remaining data points that were not selected.</p>

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Bibliographic Details
Main Author:	Matheus Viana da Silva (21433572) (author)
Other Authors:	Natália de Carvalho Santos (21433575) (author), Julie Ouellette (17041536) (author), Baptiste Lacoste (10536857) (author), Cesar H. Comin (3724186) (author)
Published:	2025
Subjects:	Cell Biology Genetics Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> creating poor inference quality available metadata information training sets lead neural networks trained datasets traditionally used selecting relevant samples image acquisition process good contrast leads base dataset using training samples results training samples used neural network four samples considering samples vessmap dataset usually required single image several hours segmentation algorithms possible changes new dataset might affect manual annotation large non introduce vessmap intensity variability imaged tissues image annotation highly distinct generalization capability even outlier especially true distribution shifts different conditions dice scores dice score demanding task carefully selected careful selection assorted set
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Summary:	<p><i>k</i> random points (green dots) are drawn from the sampling set (blue dots). The subset of sampled data points is defined by the data points that are closest to each drawn point (orange stars). Red squares represent the remaining data points that were not selected.</p>

Illustration of the proposed sampling protocol.

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