4 ms·

I think you're missing a very important part here that maybe you've considered: Domain knowledge. I'm assuming your radiologic images were hand labeled by other

I think you're missing a very important part here that maybe you've considered: Domain knowledge. I'm assuming your radiologic images were hand labeled by other radiologists. How did they come to that diagnosis? By only looking at the image? This was a severe limitation of the Andrew Ng paper on CheXnet for detection of pneumonia from chest x Rays. CheXnet was able to outperform radiologists on detection of pneumonia from the chest x Rays, but the diagnosis of pneumonia is considered a clinical diagnosis that requires more information about the patient. My point is that while your results are impressive and indicative of where deep learning could help in medicine, these same results might be skewed since you're testing the model on hand labeled data. What happens if you apply this in the real world at a hospital where the radiologist gets the whole patient chart and your model only gets the x Ray?

bitL 8y ago
There is a paper discussing higher-order interdependencies between diagnoses [1] on X-ray images (they seem to apply LSTM to derive those dependencies). This could be probably extended to include data outside X-ray images. My take is that it's pretty impressive what we can derive from a single image; now if we have multiple low-level Deep Learning-based diagnostic subsystems and combine them together via some glue (either Deep (Reinforcement) Learning, classical ML, logic-based expert system, PGM etc.), we might be able to represent/identify diagnoses with much more certainty than any single individual M.D. possibly could (also creating some blindspots that humans won't leave unaddressed). It could be difficult to estimate statistical properties of the whole system though, but that's a problem with any complex system, including a group of expert humans. The main critique for CheXNet I've read was focused on the NIH dataset itself, not the model. The model generalizes quite well across multiple visual domains, given proper augmentation. [1] https://arxiv.org/abs/1710.10501 https://arxiv.org/abs/1710.10501

bitL 8y ago

There is a paper discussing higher-order interdependencies between diagnoses [1] on X-ray images (they seem to apply LSTM to derive those dependencies). This could be probably extended to include data outside X-ray images. My take is that it's pretty impressive what we can derive from a single image; now if we have multiple low-level Deep Learning-based diagnostic subsystems and combine them together via some glue (either Deep (Reinforcement) Learning, classical ML, logic-based expert system, PGM etc.), we might be able to represent/identify diagnoses with much more certainty than any single individual M.D. possibly could (also creating some blindspots that humans won't leave unaddressed). It could be difficult to estimate statistical properties of the whole system though, but that's a problem with any complex system, including a group of expert humans. The main critique for CheXNet I've read was focused on the NIH dataset itself, not the model. The model generalizes quite well across multiple visual domains, given proper augmentation. [1] https://arxiv.org/abs/1710.10501 https://arxiv.org/abs/1710.10501