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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 Kh

by 2mur 15y ago

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 to overall survival of the patient (so it is not as impressive as the somewhat hyperbolic HN title makes it out to be... a better title would be 'Stanford computer analyzes breast cancer more accurately than human doctor' which is not surprising at all given the well-documented interobserver variability in breast cancer scoring, particularly in moderately-differentiated tumors). That is to say that the computer already knows the tissue that it is looking at is a tumor and not benign breast tissue. Furthermore, it is really only providing a histologic (or morphologic) score, which is to say, that it is attempting to predict how aggressive the tumor will behave based upon how well or not well it is differentiated (how ugly it is). These days, this score is actually less useful in clinical practice than other information such as whether or not the tumor cells are expressing estrogen/progesterone hormone receptors, or if it is over-expressing Her2-neu protein, as these are possible paths for cancer therapy (anti-estrogen drugs vs. Herceptin) in addition to being prognostic indicators (tumors which express ER/PR generally behave better than tumors which are ER/PR negative and express Her2), as well as the stage (or how far the cancer has already spread) at the time the patient is diagnosed. There are a bunch of companies which are getting FDA approval for computer vision related algorithms for scoring immunohistochemical assays for ER/PR/Her2 [1]. 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. I am far more concerned about molecular diagnostics. That is the true-future for making cancer determinations and may even eliminate the part of my job where I tell you if something is benign vs. malignant. [1] http://www.aperio.com/pathology-services/analyze-slides-automated-pathology.asp http://www.aperio.com/pathology-services/analyze-slides-auto...

bh42222 15y ago
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. Can you elaborate?
marshallp 15y ago
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.
- tptacek 15y ago
  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.
  - marshallp 15y ago
    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 just haven't spread widely anough yet.
    - apu 15y ago
      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.

by 2mur 15y ago

bh42222 15y ago

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. Can you elaborate?

marshallp 15y ago

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.

tptacek 15y ago
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.
- marshallp 15y ago
  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 just haven't spread widely anough yet.
  - apu 15y ago
    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.