> When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?"
So here you say quite a mouthful. If you train it on a pattern it'll see that pattern everywhere - think about the early "Deep Dream" trippy-dogs-pictures nonsense that was pervasive about eight or nine years ago.
I repaired a couple of cameras for someone who was working with a large university hospital about 15 years ago, where they were using admittedly 2010s-era "Deep Learning" to analyse biopsy scans for signs of cancer. It worked brilliantly, at least with the training materials, incredible hit rate, not too terrible false positive rate (no biggie, you're just trying to decide if you want to investigate further), really low false negative rate (if there was cancer it would spot it, for sure, and you don't want to miss that).
But in real-world patient data it went completely mental. The sample data was real-world patient data, too, but on "uncontrolled" patients, it was detecting cancer all over the place. It also detected cancer in pictures of the Oncology department lino floor, it detected cancer in a picture of a guy's ID badge, it detected cancer in a closeup of my car tyre, and it detected cancer in a photo of a grey overcast sky.
Aw no. Now what?
Well, that's why I looked at the camera for them. They'd photographed the biopsies with one camera on site, from "real patients", but a lot of the "clear" biopsies were from other sites.
You're ahead of me now, aren't you?
The "Deep Learning" system had in fact trained itself on a speck of shit on the sensor of one of the cameras, the one used for most of the "has cancer" biopsies and most of the "real patient under test" biopsies. If that little blob of about a dozen slightly darker pixels was present, then it must be cancer because that's what the grown-ups told it. The actual picture content was largely irrelevant because the blob was consistent across all of them.
I'm not too keen on AI in healthcare, not as a definitive "go/no-go" test thing.
So here you say quite a mouthful. If you train it on a pattern it'll see that pattern everywhere - think about the early "Deep Dream" trippy-dogs-pictures nonsense that was pervasive about eight or nine years ago.
I repaired a couple of cameras for someone who was working with a large university hospital about 15 years ago, where they were using admittedly 2010s-era "Deep Learning" to analyse biopsy scans for signs of cancer. It worked brilliantly, at least with the training materials, incredible hit rate, not too terrible false positive rate (no biggie, you're just trying to decide if you want to investigate further), really low false negative rate (if there was cancer it would spot it, for sure, and you don't want to miss that).
But in real-world patient data it went completely mental. The sample data was real-world patient data, too, but on "uncontrolled" patients, it was detecting cancer all over the place. It also detected cancer in pictures of the Oncology department lino floor, it detected cancer in a picture of a guy's ID badge, it detected cancer in a closeup of my car tyre, and it detected cancer in a photo of a grey overcast sky.
Aw no. Now what?
Well, that's why I looked at the camera for them. They'd photographed the biopsies with one camera on site, from "real patients", but a lot of the "clear" biopsies were from other sites.
You're ahead of me now, aren't you?
The "Deep Learning" system had in fact trained itself on a speck of shit on the sensor of one of the cameras, the one used for most of the "has cancer" biopsies and most of the "real patient under test" biopsies. If that little blob of about a dozen slightly darker pixels was present, then it must be cancer because that's what the grown-ups told it. The actual picture content was largely irrelevant because the blob was consistent across all of them.
I'm not too keen on AI in healthcare, not as a definitive "go/no-go" test thing.