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Cake day: October 28th, 2023

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  • I’d like to do the same, but atm I use nginx to serve all the web interfaces… And keycloak support is either a plus subscription feature or made to work with hacky Lua scripts.

    So for now it’s security through obscurity, I got a wildcard cert and the pages are accessed based on subdomain. So afaik nobody has a clue unless they start iterating common subdomain names. (At some point™️ I’m adding proper auth though)










  • FierySpectre@lemmy.worldtoScience Memes@mander.xyzBreast Cancer
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    3 months ago

    For the image-only DL model, we implemented a deep convolutional neural network (ResNet18 [13]) with PyTorch (version 0.31; pytorch.org). Given a 1664 × 2048 pixel view of a breast, the DL model was trained to predict whether or not that breast would develop breast cancer within 5 years.

    The only “innovation” here is feeding full view mammograms to a ResNet18(2016 model). The traditional risk factors regression is nothing special (barely machine learning). They don’t go in depth about how they combine the two for the hybrid model, so it’s probably safe to assume it is something simple (merely combining the results, so nothing special in the training step). edit: I stand corrected, commenter below pointed out the appendix, and the regression does in fact come into play in the training step

    As a different commenter mentioned, the data collection is largely the interesting part here.

    I’ll admit I was wrong about my first guess as to the network topology used though, I was thinking they used something like auto encoders (but that is mostly used in cases where examples of bad samples are rare)