From the webpage:
The 2018 Data Science Bowl offers our most ambitious mission yet: Create an algorithm to automate nucleus detection and unlock faster cures.
Three months. $100,000.
Even if you “lose,” think of the experience you will gain. No losers.
Enjoy!
PS: Just thinking outloud but if:
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This dataset contains a large number of segmented nuclei images. The images were acquired under a variety of conditions and vary in the cell type, magnification, and imaging modality (brightfield vs. fluorescence). The dataset is designed to challenge an algorithm’s ability to generalize across these variations.
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isn’t the ability to generalize, with lower performance a downside?
Why not use the best algorithm for a specified set of data conditions, “merging” that algorithm so to speak, so that scientists always have the best algorithm for their specific data set.
So outside the contest, perhaps recognizing the conditions of the images are the most important subjects and they should be matched to the best conditions for particular algorithms.
Anyone interested in collaborating on a topic map entry?