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Comparison of semi-supervised learning methods for High Content Screening quality control. (arXiv:2208.04592v1 [cs.CV])
cs.LG updates on arXiv.org arxiv.org
Progress in automated microscopy and quantitative image analysis has promoted
high-content screening (HCS) as an efficient drug discovery and research tool.
While HCS offers to quantify complex cellular phenotypes from images at high
throughput, this process can be obstructed by image aberrations such as
out-of-focus image blur, fluorophore saturation, debris, a high level of noise,
unexpected auto-fluorescence or empty images. While this issue has received
moderate attention in the literature, overlooking these artefacts can seriously
hamper downstream image processing tasks …
arxiv comparison cv learning quality semi-supervised semi-supervised learning supervised learning