Attention-effective multiple instance learning on weakly stem cell colony segmentation. (arXiv:2203.04606v1 [eess.IV])

The detection of induced pluripotent stem cell (iPSC) colonies often needs
the precise extraction of the colony features. However, existing computerized
systems relied on segmentation of contours by preprocessing for classifying the
colony conditions were task-extensive. To maximize the efficiency in
categorizing colony conditions, we propose a multiple instance learning (MIL)
in weakly supervised settings. It is designed in a single model to produce weak
segmentation and classification of colonies without using finely labeled
samples. As a single model, we …

arxiv attention colony learning segmentation stem cell