Anthony A. Gatti

@aagatti.bsky.social

Dad | Husband | @McMasterU PhD | @StanfordRad & @WuTsaiAlliance Postdoc | @CIHR_IRSC Fellow | Runner. Biker. Adventurer. Big science geek. http://anthonygattiphd.com

This ☝🏽 💯 If 🇨🇦 created a thriving research environment by investing in tri-council & ditching the PJT focused approach that kills investment in long term programs - we would ATTRACT international talent vs bribing them to move here; to only leave later when they see there’s no sustainable funding!

I can't* fathom why the top picture, and not the bottom picture, is the standard diagram for an autoencoder. The whole idea of an autoencoder is that you complete a round trip and seek cycle consistency—why lay out the network linearly?

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“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material. In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵

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We are starting to see some nuanced discussions of what it means to work with advanced AI in its current state In this case, GPT-5 Pro was able to do novel math, but only when guided by a math professor (though the paper also noted the speed of advance since GPT-4) The reflection is worth reading.

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Methods like NeRF and Gaussian Splats model the world as radioactive fog, rendered using alpha blending. This produces great results.. but are volumes the only way to get there?🤔 Our new SIGGRAPH'25 paper directly reconstructs surfaces without heuristics or regularizers.

I love this type of approach - for imaging, biomechanics, or other domains… Take high quality dense data, use physics simulators to generate synthetic sparse data, then train a model to recon dense data from sparse measurements. Lots of potential pitfalls, but the concept is immensely powerful.

Radiology: Artificial Intelligence@radiology-ai.bsky.social · last yr.

Pixel-level lung thickness maps for real and synthetic radiographs generated with a U-Net trained on synthetic data https://doi.org/10.1148/ryai.240484 #ChestRad #LungVolume #ChestXray

Image from article in Radiology: Artificial Intelligence

Are there useful or interesting ways to use LLMs other than prompting them? I feel like compressing all the text in the world via a hierarchically structured statistical model is probably useful, but that we're using it in a way that is unlikely to do what we'd hope.

Our Siemens 7T Collaboration Scientist is moving to support the Siemens 7T Terra.X Impulse at Stanford. Siemens is rehiring for our position, located in Canada's National Ultra-High Field MRI platform cfmm.uwo.ca in London, Ontario 🇨🇦. onehealthineers.wd3.myworkdayjobs.com/SHSJB/job/OA...

Centre of Functional and Metabolic Mapping - Western University

Western University, in vibrant London, Ontario, delivers an academic and student experience second to none.

cfmm.uwo.ca

Government spending on fundamental research is somewhat pointless (from the economic development perspective!) unless there are mechanisms to capture developments that could have commercial value in this country. We give away or license most of this outside 🇨🇦. That is a BIG problem.