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Stanford computer diagnoses breast cancer more accurately than human doctor

extremetech.com

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Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#41

Post by tryitnow »

bh42222 wrote: post #15

This should not be surprising for anyone who understand software. And software identifying breast and skin and other cancers is going to continue getting better. Now the bad news is that human medicine moves super slowly due to very strict regulations, and I expect doctors to resist this. I hate to say this, but I think health insurance companies are our best hope to push this technology into greater use.

I agree. A lot of these technologies will lead to improved outcomes at lower costs (for certain sets of basic problems).

Guild professions (like doctors) are inclined to keep doing even the basic simple-minded aspects of their job because they get paid "economic rents" for doing so thanks to the regulations that insist even basic tasks must be done by someone with 10+ years of education.

Insurance companies on the other hand ultimately have to respond to employer demands for lower premiums (unfortunately this process is slow and HR departments are usually horrible at keeping costs under control). Insurers and to an extent employers are going to be the impetus for a lot of improvements in effectiveness and affordability.

Read Clayton Christensen "The Innovators Prescription" for more on how this might play out.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#42

Post by bayesftw »

malingo wrote: post #25
2mur wrote: post #16

I'm a pathologist and an avocational programmer. This is pretty neat material and is very relevant to me, as I have been trying to bone up my math chops with Khan Academy videos so that I can tackle some computer vision related work in pathology. With regards to the study, I will just point out that the system is not diagnosing breast carcinoma, but rather is producing a score which reflects the prognosis as relates…

with your knowledge as a pathologist, would you agree that the success of this approach depends on the quality of the biopsy (i.e. obtaining a sample that faithfully represents the entire volume of tissue being investigated)? From my experience with prostate cancer screening research I know that biopsy scoring of prostate tumors can be very inaccurate compared to pathology scoring after a prostatectomy.

Not the OP, but a pathologist in training. Yes, quality is essential because depending on how the sample is prepared the image can be very difficult to interpret. Biopsy scoring of prostate tumors is difficult because a lot of the scoring takes into account features that may not appear on the biopsy because of insufficient sampling.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#44

Post by apu »

marshallp wrote: post #29
tptacek wrote: post #26

Earlier quoted context omitted.

That's not at all what he said. He said that this particular research involved a simpler (for CS) problem than either the title or his day to day job tackles. Do you have a background in bio or medicine or computer vision? It's very interesting to see two informed people disagree about applied computer science, so I'd love you to contribute something more specific to the thread.

Actually, he also said that he is not concerned that machine learning could initially detect cancer anytime soon. Also, if you've been machine learning trends recently(past 5 years), you'll see that deep learning methods (hinton, lacunn, ng, bengio) have actually made a huge leap over what came before, and are believed to be that "final" in some sense algorithm that can allow to tackle any learning problem. These jus…

As a computer vision researcher, I'm not at all convinced that deep learning methods will be "final" in any sense. I know that in the past, neural networks were "final", and then graphical models were "final", and so on.

And while deep learning methods have indeed shown remarkable improvements recently, they're not yet state-of-the-art on the most important/relevant computer vision benchmarks.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#45

Post by nazgulnarsil »

pak wrote: post #27

Please: there ought to be a rule on HN that instead of linking to a popsci re-interpretation of a scientific result, you instead link directly to the paper. http://stm.sciencemag.org/content/3/108/108ra113.full If it is behind a paywall, as it probably is for this paper if you are not at a university, perhaps look around for the least hyperbolic re-interpretation of the paper, and link that instead. http://www.geneng…

The cottage industry of drawing fanciful conclusions from tiny data sets needs to die in a fire. It is pure noise.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#46

Post by nitrogen »

law wrote: post #28
2mur wrote: post #16

I'm a pathologist and an avocational programmer. This is pretty neat material and is very relevant to me, as I have been trying to bone up my math chops with Khan Academy videos so that I can tackle some computer vision related work in pathology. With regards to the study, I will just point out that the system is not diagnosing breast carcinoma, but rather is producing a score which reflects the prognosis as relates…

So I am actually far less concerned about a computer doing my job very well, which is actually looking at a piece of tissue on a slide and making a tumor versus not-tumor distinction. This is very hard to do and I think will continue to be even harder for computers/computer-vision/AI to do for a long time to come. As a machine learning researcher, I do not disagree that it's "very hard" to make a single tumor-versus-…

Right now, I'm doing a much different kind of research: applying machine learning algorithms to legal documents (court opinions, statutes, patents, etc.) to generate legal analyses.

I hope that one day we can apply your (or similar) classification technology to run semi-automated regression tests on new legislation. For example, each time a new law is passed, the classification software flags any court cases that may have a different outcome as a result.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#47

Post by fgimenez »

pak wrote: post #27

Please: there ought to be a rule on HN that instead of linking to a popsci re-interpretation of a scientific result, you instead link directly to the paper. http://stm.sciencemag.org/content/3/108/108ra113.full If it is behind a paywall, as it probably is for this paper if you are not at a university, perhaps look around for the least hyperbolic re-interpretation of the paper, and link that instead. http://www.geneng…

I'm a colleague of the author, and I have talked to him quite a bit about C-Path since I work on something related. While I agree with much of what you are saying, I disagree slightly with your comment that the article is "principally, about a new morphological feature that the researchers believe is tied more strongly to survival according to their computational model."

The significance here is that he extracted 6000 low-level morphological features without any pre-conception about their usefulness. He then used GLMNET (logistic regression with L1-regularization) to automatically pick which of these features was important. Then, the craziest part is that the most informative features were not even cancer cells, but rather, surrounding stromal tissue. To quote from the paper, "Pathologists currently use only epithelial features in the standard grading scheme for breast cancer and other carcinomas. Our findings suggest that evaluation of morphologic features of the tumor stroma may offer significant benefits for assessing prognosis." He essentially took a completely blinded, machine learning technique to find features that have been relatively ignored in pathology.

I think this is more indicative of a new paradigm in computer vision and machine learning in general that finely-tuned, human-crafted features can be beat with more automatic methods. Whereas before, we have tried to program features that characterize what we see, now we are finally looking at image features that can characterize what we're missing.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#48

Post by marshallp »

apu wrote: post #44
marshallp wrote: post #29

Earlier quoted context omitted.

Actually, he also said that he is not concerned that machine learning could initially detect cancer anytime soon. Also, if you've been machine learning trends recently(past 5 years), you'll see that deep learning methods (hinton, lacunn, ng, bengio) have actually made a huge leap over what came before, and are believed to be that "final" in some sense algorithm that can allow to tackle any learning problem. These jus…

As a computer vision researcher, I'm not at all convinced that deep learning methods will be "final" in any sense. I know that in the past, neural networks were "final", and then graphical models were "final", and so on. And while deep learning methods have indeed shown remarkable improvements recently, they're not yet state-of-the-art on the most important/relevant computer vision benchmarks.

As a computer vision researcher it must be pain you to see that all your learnings are for nought when faced with deep learning methods which can get amazing performances from raw pixels (see mnist results for example). Also see ronan collobert's natural language processing from scratch paper where handily beats the past few decades of nlp research in parsing (in terms of efficiency, and probably performances soon too). Or see the microsoft research speech recognition swork which has beaten out all previous by a significant margin using deep learning.

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#49

Post by Gatsky »

marshallp wrote: post #21
2mur wrote: post #16

I'm a pathologist and an avocational programmer. This is pretty neat material and is very relevant to me, as I have been trying to bone up my math chops with Khan Academy videos so that I can tackle some computer vision related work in pathology. With regards to the study, I will just point out that the system is not diagnosing breast carcinoma, but rather is producing a score which reflects the prognosis as relates…

I've seen a lot of doctors chime in various threads and say their jobs couldn't possible be done by machine learning. The same thing was said about self driving cars before the darpa challenges - when some profs actually put their mind to it, it was done in a couple of years. . If the data was available, there is probably quite a few people who can actually detect cancer in slides.

I think you are misunderstanding the nature of the problem. There is no easy way to assess how good some mythical algorithm will be at interpreting pathology slides. Therefore, you are in essence asking doctors and patients to accept another non-human opinion about what is going on. So why should I accept your algorithm's opinion? I would rather have a human who has enough insight to say they are not sure and can discuss the case with me, and also understands that life changing decisions are being made on the basis of what they say.

Anyway, pathologists are most useful in unusual or difficult cases, which by definition have little available data. You want me to trust an algorithm trained using some kind of statistical mechanic on a dataset to interpret an edge case?

Re: Stanford computer diagnoses breast cancer more accurately than human doctor

#50

Post by Gatsky »

codeslush wrote: post #31
2mur wrote: post #16

I'm a pathologist and an avocational programmer. This is pretty neat material and is very relevant to me, as I have been trying to bone up my math chops with Khan Academy videos so that I can tackle some computer vision related work in pathology. With regards to the study, I will just point out that the system is not diagnosing breast carcinoma, but rather is producing a score which reflects the prognosis as relates…

You say "particularly in moderately-differentiated tumors" -- I have a few questions. 1. Why did you specifically point out "moderately-differentiated?" I ask because my wife has a tumor classified as poorly-differentiated. I'm wondering if the middle ground is unique/harder to diagnose in some way. 2. Would something like this come into play during the initial biopsy or after the tumor is removed? I ask because she…

Again, best wishes for your family.

I think that C-Path is interesting, but it is not well validated at present. It is also not clear how to use the results to plan treatment.

HER2 status is your ideal marker of risk because it gives information about prognosis AND how to treat (give herceptin). A bad stroma score or whatever you want to call it on C-Path doesn't necessarily tell you what to do about it. I would say that C-Path would add little to your wife's care if the cancer is HER2 positive, as treatment in this case is usually indicated.

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