Deep learning outperformed dermatologists in melanoma image classification task
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Re: Deep learning outperformed dermatologists in melanoma image classification task
#2I've always thought that diagnostic-oriented specialties would be most at risk (pathology, dermatology, radiology, ophthalmology).
As long as you have procedures, you will have a need for an extremely competent clinician that can synthesize all information and coordinate with use of hands or devices.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#3I wonder if the results would be similar were the dermatologists to see the actual patient, in person, and then diagnose. And, then a photo was taken to be diagnosed by the CNN.
In other words, while dermatologists may have been outperformed by deep learning in image classification, it is not evident if deep learning could do the same against dermatologist diagnosing in person.
Also, not clear what the overall ratio of false negative/positives was in each case.
Also, unless I missed it in the paper, I'd be curious to learn if the cases, in Fig. 4, where majority of humans and CNN disagreed, were the same where majority of humans disagreed too or not.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#4This is a great example of where we need to get humans out of the equation when (if) a machine is conclusively proven to perform consistently better.
It was justified (cost wise) to replace many human labourers on auto assembly since machines don't get tired, need breaks, have off days. It could certainly be argued it is even more important in the field of health care (reduce costs and improve outcomes) for all forms of image scanning.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#5It's hard to imagine an narrow image classification task that humans will be able to beat NNs.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#6As always, let's see how well it does in live images. This system outperformed dermatologists on its own validation set of 100 images, which I would encourage you to interpret as "heartening preliminary evidence" but not much more.
Posting high scores on your validation set is only as informative as your val set is representative of the real world. 70% specificity, 84% sensitivity looks OK on paper (maybe -- as another poster noted, it's equally fair to say it's good evidence that image-only diagnosis is bad no matter what does it), but it doesn't always feel that way in practice. As a cheap example, your word error rate for a speech recognition system has to be extremely low in order for that system to be nice to use -- way lower than most otherwise acceptable looking scores.
This analogy only gets you so far, and i don't mean to impugn this study's test set, but another example is just because you can post 99.9% on MNIST doesn't mean that your system will approach that level of accuracy on digit recognition in the wild.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#7Interesting, however there is no indication from the publisher or researchers how this result can be reproduced. It's nice that they put in some of the training data, but imagine how much more impactful to the community this could be if those interested could reproduce - and iterate - on this...
At Gigantum (https://github.com/gigantum/gigantum-client) this is literally our raison d'être to make this process as simple as possible.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#8I find it most interesting to use tools like this to augment - not replace diagnosticians and specialists.
Of course the key here is that the training set is crucial to building a high quality model - which of course needs a set of specialists to give their consensus on the diagnosis of the patient based on the images.
Presuming those folks can agree - the technology becomes a force multiplier for good. If they disagree or label things problematically - they become a force multiplier for bad.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#9This is a great example of where we need to get humans out of the equation when (if) a machine is conclusively proven to perform consistently better. It was justified (cost wise) to replace many human labourers on auto assembly since machines don't get tired, need breaks, have off days. It could certainly be argued it is even more important in the field of health care (reduce costs and improve outcomes) for all forms…
Machines will never replace dermatologists, machines will only make them more efficient.
Re: Deep learning outperformed dermatologists in melanoma image classification task
#10I find it most interesting to use tools like this to augment - not replace diagnosticians and specialists. Of course the key here is that the training set is crucial to building a high quality model - which of course needs a set of specialists to give their consensus on the diagnosis of the patient based on the images. Presuming those folks can agree - the technology becomes a force multiplier for good. If they disag…
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