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Robust Asymmetric Loss for Multi-Label Long-Tailed Learning. (arXiv:2308.05542v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
In real medical data, training samples typically show long-tailed
distributions with multiple labels. Class distribution of the medical data has
a long-tailed shape, in which the incidence of different diseases is quite
varied, and at the same time, it is not unusual for images taken from
symptomatic patients to be multi-label diseases. Therefore, in this paper, we
concurrently address these two issues by putting forth a robust asymmetric loss
on the polynomial function. Since our loss tackles both long-tailed and …
arxiv data diseases distribution images labels loss medical medical data multiple patients show training