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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

by law 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. As a machine learning researcher, I do not disagree that it's "very hard" to make a single tumor-versus-not-tumor classifications. However, machine-learned classifiers are becoming much more adept at making these types of classifications, and will only improve with time. I think most of the problem right now is in creating a corpus with enough attributes and precise/reliable measurements to accurately train a classifier. Presently, computer vision algorithms are quite sensitive to noise; the human eye is much better at separating this noise from the actual signal. 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. To not worry about a computer "doing your job very well" is, in my opinion, a naive perspective. It's indisputable that one day, computers will be doing classification-type jobs very well. Embracing the technology will keep you ahead of the curve; dismissing it is suicide.

2mur 15y ago
Thanks. I probably should have caveated that statement with some qualifier. I'm not particularly worried about it in the short-term timeframe in which I expect to be earning my bread as a practicing surgical pathologist. I absolutely expect at some point, machine learning will have progressed to the point to be able to perform my job as well as I can. I just think that using it in a clinical setting is a long way off, and far before we get to that point, we will be using pure molecular techniques to diagnose and classify cancer. If I had to list the tasks that I would imagine machine learning to be able to do at an expert level before my eye/brain combination could be replaced for diagnosing cancer on glass slides it may look like: - Segregate tissue from non-tissue (ie. blank slide space) - Classify the tissue (epithelial versus mesenchymal/stromal, mucin/matrix, exogenous material, etc). - Decide if it is normal or abnormal - Decide if it is native to whatever organ that it reportedly came from or is it a metastases from somewhere else. - Select appropriate immunohistochemical stains or other ancillary tests (eg. molecular assays). - Appropriately interpret the immunohistochemical stain (eg. is it a nuclear stain or cytoplasmic, or both, or membranous). Is that staining true staining or artifactual. - Interpret that in the clinical context of the patient's history. And it has to do all of those things and correct and account for bad preparations from the laboratory, inconsistent staining from day-to-day and slide-to-slide, non-representative biopsies from the surgeons etc. I think at the end of my career, they will eventually haul my carcass off of my microscope (hopefully!) in 40 to 50 years hence. I imagine that we will have made a ton of progress with computer vision in pathology, and will be using it in an adjunct fashion. To help with analyzing results and other tedious tasks that computer vision is well-suited for. But a lot of medicine, and especially pathology, is making intuitive connections and conclusions with inconsistent and incomplete evidence in day-to-day work on individual patients.
- law 15y ago
  Yeah, that's exactly the type of 'noise' I was referring to. Improper histological staining, poor imaging skills, etc. are all things that the human mind can immediately discover. The human mind is smart enough to not rely on the assumption that all random variables implicated are conditionally independent of each other. Computers, on the other hand, use algorithms rooted in these assumptions.
nitrogen 15y ago
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.

by law 15y ago

2mur 15y ago

Thanks. I probably should have caveated that statement with some qualifier. I'm not particularly worried about it in the short-term timeframe in which I expect to be earning my bread as a practicing surgical pathologist. I absolutely expect at some point, machine learning will have progressed to the point to be able to perform my job as well as I can. I just think that using it in a clinical setting is a long way off, and far before we get to that point, we will be using pure molecular techniques to diagnose and classify cancer. If I had to list the tasks that I would imagine machine learning to be able to do at an expert level before my eye/brain combination could be replaced for diagnosing cancer on glass slides it may look like: - Segregate tissue from non-tissue (ie. blank slide space) - Classify the tissue (epithelial versus mesenchymal/stromal, mucin/matrix, exogenous material, etc). - Decide if it is normal or abnormal - Decide if it is native to whatever organ that it reportedly came from or is it a metastases from somewhere else. - Select appropriate immunohistochemical stains or other ancillary tests (eg. molecular assays). - Appropriately interpret the immunohistochemical stain (eg. is it a nuclear stain or cytoplasmic, or both, or membranous). Is that staining true staining or artifactual. - Interpret that in the clinical context of the patient's history. And it has to do all of those things and correct and account for bad preparations from the laboratory, inconsistent staining from day-to-day and slide-to-slide, non-representative biopsies from the surgeons etc. I think at the end of my career, they will eventually haul my carcass off of my microscope (hopefully!) in 40 to 50 years hence. I imagine that we will have made a ton of progress with computer vision in pathology, and will be using it in an adjunct fashion. To help with analyzing results and other tedious tasks that computer vision is well-suited for. But a lot of medicine, and especially pathology, is making intuitive connections and conclusions with inconsistent and incomplete evidence in day-to-day work on individual patients.

law 15y ago
Yeah, that's exactly the type of 'noise' I was referring to. Improper histological staining, poor imaging skills, etc. are all things that the human mind can immediately discover. The human mind is smart enough to not rely on the assumption that all random variables implicated are conditionally independent of each other. Computers, on the other hand, use algorithms rooted in these assumptions.

nitrogen 15y ago

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.