Calibrating Histopathology Image Classifiers using Label Smoothing. (arXiv:2201.11866v1 [eess.IV])

Web: http://arxiv.org/abs/2201.11866

The classification of histopathology images fundamentally differs from
traditional image classification tasks because histopathology images naturally
exhibit a range of diagnostic features, resulting in a diverse range of
annotator agreement levels. However, examples with high annotator disagreement
are often either assigned the majority label or discarded entirely when
training histopathology image classifiers. This widespread practice often
yields classifiers that do not account for example difficulty and exhibit poor
model calibration. In this paper, we ask: can we improve model calibration …

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