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tasuki | on: A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis
> I compared to a 10-year old CNN and to my surprise found that this was superior.

I'm surprised he's surprised.

> A small, old, specialised network using an outdated architecture recognises skin lesions far better than two frontier LLMs.

Yes of course. A 30m params Katago network with one playout is superhuman in playing go, while frontier LLMs have over 10,000 times more parameters and aren't any good at playing go. They're good at other things (such as helping you create a network that's good at playing go).

jrflo | parent | next
A CNN is also definitely not an “outdated architecture” when it comes to image classification
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sungam | parent | next
I think the reason I am surprised is that they are exceptionally good at interpreting images generally and there are tens of thousands of labeled skin cancer images on the public internet so I had assumed the general image interpretation abilities would transfer
ang_cire | root | parent | next
Having actually worked with teams doing medical vision models for cancer detection, I can tell you that the datasets we used were not public, and they came from studies and private hospital partnerships that LLM companies don't have access to.
jrflo | root | parent
The specialized medical diagnosis models are trained on that same data though, and also use neural networks. The more noteworthy story will be when they overtake specialized models.
gradascent | root | parent
Not only do they train on that data but they train ONLY on that data and don’t have to generalize to other domains. And the architectures themselves, including hyperparameters, loss function, etc are chosen and tuned specifically for the quirks of this domain. So this result doesn’t surprise me either.
ip26 | parent
such as helping you create a network that's good at playing go

This is really what’s happening right now… LLMs are excellent computer users. Which makes one of the frontiers “amazing things computers always could have done, but we lacked the programmers to do”.

If basically anyone can train a high quality CNN for just about any purpose now, what new opportunities appear that computers can help with?