Today is the day! @ImarisSoftware releases their version 9.9, prominently featuring Labkit and the ImgLib2-Imaris bridge.
What this means for you, for Imaris, and for @FijiSc you ask?
A mini-🧵…
frontiersin.org/articles/10.33… Labkit is one of the oldest projects in the @jug_lab — a data labeling tool and random forest pixel classifier! Useful but not the only tool of its kind. (Friendly shoutout to @ilastik_team, Trainable Weka, etc.)
What makes Labkit special is what’s going on under the hood…

🚨 ICLR SPOTLIGHT 🚨
Unsupervised denoising, i.e. removing noise in data without having ground truth, has a new state-of-the-art. A short 🧵 about why this rocks...
openreview.net/forum?id=DfMql…
Spoiler: it can also remove structured noise & gives diverse solutions for ambiguous data Our method, called HDN, employs a hierarchical VAE to learn a latent representation of structures occuring in a noisy body of data. Hence, we can later use the model to sample a space of sensible ‘data interpretations’.

Ops, we did it again! Image denoising just leveled up!!! arxiv.org/abs/2006.06072
Our new method can do so much more than just predicting a denoised image!
Let us take you on a short tour… (1/6) #CARE #denoising #uncertainty #diversity @Mangal_Prakash_ @sagzehn @jug_lab What always bugged us with any CARE method is that a single restored output is created, giving the false impression that the input can unambiguously be restored. But some information gets lost in the noise, so this can obviously not strictly be true! (2/6) arxiv.org/abs/2006.06072