In the medical field, certainty is critical, but understanding uncertainty is equally important. “It’s crucial we can detect cases where the model is uncertain,” Kim tells us. “If we always assume our model to be 100% certain, it would be impractical for clinical relevance because a doctor would need to check these cases.” For example, when segmenting lung lesions, it is essential to identify cases where the model fails to detect the tumor outline accurately. Implementing a failure detection mechanism in the segmentation model enables automatic flagging of potentially inaccurate segmentations so that they can be reviewed by a human annotator or 13 Computer Vision News Computer Vision News ValUES: A Framework for Systematic …
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