That was a tough one. Did he forget how many outs there were? Looked like even after the bobble he had plenty of time to throw to first.

Btw, and in the spirit of #OpenScience, all my #Napari #bioimage analysis #tutorials are freely available — from basics to #segmentation, #tracking, and #clustering. Feel free to explore and share ✌️

🌍 www.fabriziomusacchio.com/teaching/tea...

Giraffe spots would be something to measure that would be corrupted by the noise too. So would want to keep them and after restoration it would be nice to be able to count the correct number of spots, shape of spots and (importantly ) intensity of spots...

Even with the really good microsam/cellposesam generalist models, annotating specialist labels and retraining is always better.

Love to hear more about testing. I'm always skeptical when I hear "new method B" outperforms previous method "A" because it often turns out "A" was not run optimally. Seems reasonable in an unsupervised or general mode this new method could be better than generalist Stardist/Cellpose but ...

You will be missed. I really appreciated you getting the North East Bioimaging meeting organized the last couple years.

Where did you order it? Seems like the retail price is suppose to be about $2000, however online they peaked at like $5000 a month ago, but seem to be down to $3000 now.... still way above suggested retail price.

2) Machine Learning - low labeling work/low training time and resources. Sparse-label deep learning low labeling work/high training time and resources. Dense deep learning high labeling work/high training time and resources. So which one to use based on the trade offs?

1) Hi @haesleinhuepf.bsky.social on this topic are you aware of any good white papers or blogs on different approaches to semantic segmentation specifically Machines Learning vs Sparse-label deep learning vs Dense-label deep learning? So basically....