Wacom Cintiq Pro στην ιατρική
Το 2026, η έρευνα σε medical imaging και semantic segmentation έχει γίνει τόσο πυκνή σε δεδομένα και τόσο απαιτητική σε λεπτομέρεια, ώστε η έννοια του “pixel‑wise” δεν είναι πια θεωρητική – είναι η καθημερινότητα στα εργαστήρια και στα κλινικά τμήματα. Σε αυτό το πλαίσιο, η Wacom Cintiq Pro έχει […]
We're reintroducing and open-sourcing project "See-through". Given a single anime illustration, it automatically decomposes the character into fully-inpainted semantic layers with depth ordering. One image in, layered PSD out. (1/n)
Repo:
https://github.com/shitagaki-lab/see-through…
https://x.com/ljsabc/status/2038907277017776441
#anime #imagesegmentation #semanticsegmentation #depthestimation #opensource
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Functional stay-green supports complete grain filling in wheat genotypes, but must be carefully distinguished from cosmetic variants. Image-based monitoring of leaf senescence and canopy temperature dynamics can differentiate between ideotypic and cosmetic stay-green in high-yielding environments.
https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1335037/full
#Thermography, #StayGreen, #Wheat, #FieldPhenomics, #SpikeDevelopment, #ShadingStress, #SourceSinkRatio #SinkStrength #SemanticSegmentation
I am off-work today, but I am taking some time reflecting on somewhat boring (annoying?) dilemmas from work.
1) I don't like something about U-Net shaped neural networks, which are great starting points for #segmentation in #medicalimaging . In general, I think that the encoder should do most of the semantic work (see SAM from Meta AI). These networks usually have 1-2x parameters/computations on the decoder hand. Basically they operate on downsampled images then need upsampled outputs (U shape, resolution goes down, then up, and stages are "parallel").
What do?
(Context: I process 3D brain scans, much numbers and compute!)
2) I come back to similarity, proximity and distance measures, metric and semi-metric. Who's farthest from data point A? Data point B with ~-1 correlation, or data point C with ~0 correlation? If you choose to put C the farthest, how do you maintain the distinction of sign? I want point D (~1 correlation) and point B close to A, but in "different" ways ???
(Context: I cluster health records, sometimes negative correlations do not mean much, sometimes they do)
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