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This award goes to ALL authors and I must send out ❤️ and 🙏 to Martin (@martweig) who really is the 🧠 behind CARE and Uwe (@uschmidt83) without who the quality and usability of CSBDeep would not be anywhere close to what it is. @PavelTomancak helped us write a GOOD paper, and…
Congratulations to @florianjug for being awarded the 2023 ICBS Frontiers of Science award! The International Congress for Basic Science honors top research with an emphasis on achievements from the past 5 years which are both excellent and of outstanding scholarly value.
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As part of CAM/NIC teaching activities @NUFeinbergMed @NU_CDB our in-house expert image analyst David Kirchenbuchler gave a tour-de-force course covering image analysis. @FijiSc @NikonInst #Noise2Void @florianjug #CARE #CSBDeep #TensorFlow #WekaSegmentation #PCA A great teacher!
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Such a bad day! I can’t use my RTX3000 series for Fiji because I can’t use CUDA 10.0 and TF 1…. #csbdeep #imagej #fiji #care #n2v

ALT Rtx7025 GIF

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So what about CSBdeep in Napari?
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GitHub stats give adequate credits to the creators of this truly wonderful resource: @frauzufall (lion share) and @bewilh. github.com/CSBDeep/CSBDeep_f… Thanks to you two and also to all other (beyond Fiji) CSBDeep heroes!

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Just got the numbers, 13838 users had the CSBDeep update site enabled on @FijiSc since 2017. Good job @jug_lab @florianjug
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This weeks #ImageAnalysis tool is CSBDeep; a deep learning toolbox for microscopy image restoration and analysis. csbdeep.bioimagecomputing.co…

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If you’re looking for a friendly interface to interact with code, notebooks such as @ProjectJupyter or @GoogleColab notebooks are a great resource. #CSBDeep, #ZerocostDL4Mic and #CellPose prepared ready-to-use notebooks to test and/or train neural networks.
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Plugins such as #CSBDeep, #DeepImageJ, and #DeepMIB integrate deep learning into common image analysis toolboxes. These plugins are a great option for those familiar with #ImageJ @Icy_BioImaging #MicroscopyImageBrowser @ilastik_team also has deep learning tools (in beta now)
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Replying to @pushkal_sharma
Thank you @pushkal_sharma I'm afraid that you'll need to train/fine-tune a model, BUT that's not a problem any more if you have some annotated images: #ZeroCostDL4Mic is your solution. Also you can try to train #DenoiSeg in the #CSBDeep plugin for ImageJ, or use #Ilastik, #Yapic
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Don’t worry, both are from @FijiSc‘s CSBDeep... 😉 @frauzufall @jug_lab The Noisepecker is the pet of the GPU Heavy Lifter...
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Replying to @damiandn
I vote for @frauzufall's noisepecker in @FijiSc / #CSBDeep / #Noise2Void 🤓

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The #i2k2020 phenotype: 10:30 PM roaming around the island, having a beer, updating CUDA, training a network and labelling images. Good thing I expected nothing less... #CSBDeep #WeAreStarDist
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Thanks Pedro! It's really fun and the DL approaches are amazingly powerful. Especially the #CARE networks by @martweig. The implementation in #ZeroCostDL4Mic and the #CSBDeep plugin in #Fiji are super nice to use! Thanks to the entire #OpenSource #DeepLearning community!
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Only possible with the help of the Heilemann @henriqueslab @seamus_holden @guijacquemet labs and the awesome #OpenSource philosophy of the DL community! #ZeroCostDL4Mic #CSBDeep #FijiSc Source for the car AI video: tesla.com/autopilotAI

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Just wanted to mention that CSBDeep (the framework of StarDist) already supports processing in chunks 😉
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New tool available on the CSBDeep @FijiSc update site: DenoiSeg - joint denoising and segmentation method based on #N2V and #CSBDeep by @tibuch_ @Mangal_Prakash_ @sagzehn @florianjug. Details: forum.image.sc/t/denoiseg-jo… Preprint: arxiv.org/abs/2005.02987
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New back-to-back releases of our DL python packages csbdeep and stardist. Among many other things, now allows for using tf2, stitched predictions on large 2D images, and importing 3D stardist results in @blender_org :) kudos to @uschmidt83!
New Python releases for #CSBDeep and #StarDist! Lots of improvements under the hood, including support for TensorFlow 2. Joint effort with @martweig! All changes: github.com/CSBDeep/CSBDeep/r… github.com/mpicbg-csbd/stard…
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However Stardist/csbdeep models trained with tf 2 should already run with the current Fiji plugin when using the tf 1.14 backend
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