Uwe Schmidt

@[email protected]

[Uwe Schmidt @uschmidt83]

If you're interested in the details of our #StarDist-based submission to the 2022 CoNIC challenge, we finally released the code.

https://t.co/up4wh0s4ay

[Martin Weigert (maweigert.bsky.social) @martweig]

How well does #StarDist work on histopathology images? Pretty well! Our StarDist approach (with @uschmidt83) just won the nuclei seg/class task of the ISBI 2022 CoNIC challenge! 🎉
Paper: https://t.co/wNIrQp2zmp
https://t.co/QTHAsZH3nj

[Uwe Schmidt @uschmidt83]

@EmtiyazKhan Congrats!

[Uwe Schmidt @uschmidt83]

@PavelTomancak @CEITEC_Brno @muni_cz

[ilastik Team and Anna Kreshuk's Lab @ilastik_team]

Together with the wonderful collaborators and community partners, we are happy to finally present the BioImage Model Zoo at https://t.co/2eTqKyOqD2, now described in https://t.co/UxwWnKU7LH.

[Martin Weigert (maweigert.bsky.social) @martweig]

How well does #StarDist work on histopathology images? Pretty well! Our StarDist approach (with @uschmidt83) just won the nuclei seg/class task of the ISBI 2022 CoNIC challenge! 🎉
Paper: https://t.co/wNIrQp2zmp
https://t.co/QTHAsZH3nj

[Michael Black @Michael_J_Black]

I get a lot of reviews that say my work is not novel and I bet I'm not alone. It's always frustrating because I see novelty where the reviewer doesn't. Rather than rebut every critique, I've written a blog post to help reviewers think about novelty. https://t.co/UXLabOkYcn

[Emtiyaz Khan @EmtiyazKhan]

Our new paper on "The Bayesian Learning Rule" is now on arXiv, where we provide a common learning-principle behind a variety of learning algorithms (optimization, deep learning, and graphical models).
https://t.co/Kta3EGvWba
Guess what, the principle is Bayesian. A very long🧵

[Uwe Schmidt @uschmidt83]

@psteinb_ @haesleinhuepf @matplotlib @helenajambor @annebonnerdata @HIDAdigital @thecarpentries @bercowskya @NEUBIAS_COST @bokeh The scikit-image part in my repo covers some plotting with matplotlib. But it’s all tailored to biologists. https://t.co/Fz6h76mFof

[Martin Weigert (maweigert.bsky.social) @martweig]

We just made a new stardist release, one of the bigger ones! Multi-class prediction, memory/runtime gains, and integration for the shiny new napari plugin :)
As usual, great colab with @uschmidt83 and many others: https://t.co/XSLpEj2Nn7

[Uwe Schmidt @uschmidt83]

@roybiolab @FijiSc I’m not sure, maybe related to this? https://t.co/5vMOyDpo9h
If that doesn’t help, please create a new topic at the https://t.co/GH1yNrsIww forum.

[Uwe Schmidt @uschmidt83]

We released a #StarDist plugin for @FijiSc! 🎉
https://t.co/xFhVOHDEsa

It ships with a versatile model that we found to work well for segmenting cell nuclei in a variety of challenging (2D) fluorescence microscopy images.

🌟 #WeAreStarDist

[Uwe Schmidt @uschmidt83]

@psobolewskiPhD @FijiSc Yes, tensorflow is the real issue when it comes to StarDist.

[Uwe Schmidt @uschmidt83]

@psobolewskiPhD @FijiSc I think Fiji can’t run native at the moment.

[Uwe Schmidt @uschmidt83]

@psobolewskiPhD @FijiSc https://t.co/fB2ktCJBW7

[Uwe Schmidt @uschmidt83]

@psobolewskiPhD @FijiSc I don’t really know. Have you tried running StarDist on a crop (1000x1000 or so) and does it work there as expected?

[Uwe Schmidt @uschmidt83]

@haesleinhuepf @napari_imaging Thanks! How did you find out about this feature? By the way, it works for me in standard Python, but not when I run my script via IPython (nothing shows up).

[Uwe Schmidt @uschmidt83]

@JwPylvanainen FYI to people only on Twitter: https://t.co/fB2ktCJBW7

[Uwe Schmidt @uschmidt83]

@ahklemm @knime @stelfrich @jan_eglinger Cute logo was made by @SuperResoluSian :)

[Uwe Schmidt @uschmidt83]

@EntraCod There is a reason why the memory grows during training (cache, cf. https://t.co/7EeH6Tr3Tw). We will add an option to disable this cache in the next release. In general, please discuss issues like this in the https://t.co/GH1yNrKjo4 forum or open an issue.

[Uwe Schmidt @uschmidt83]

@EntraCod Nobody prevents you extracting patches from your large labeled stacks upfront to reduce your training data. However, when using larger stacks for training, StarDist will dynamically sample different patches each time the stack is used.