[HN Gopher] ML model can classify sex from retinal photograph, c...
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ML model can classify sex from retinal photograph, clinicians can't
Author : jeffbee
Score : 113 points
Date : 2021-05-15 13:59 UTC (9 hours ago)
(HTM) web link (rdcu.be)
(TXT) w3m dump (rdcu.be)
| p1esk wrote:
| How long did they train humans to perform this task?
| dkarras wrote:
| I don't think we even know what to look for yet.
|
| >Clinicians are currently unaware of distinct retinal feature
| variations between males and females, highlighting the
| importance of model explainability for this task.
| NoPie wrote:
| Do we even need clinicians be able to distinguish male and
| female retinas?
|
| The task is meaningless. Yes, there might be some interesting
| facts in discovery how male and female retinas are different.
| It could even lead to differentiated treatments. But ML hasn't
| provided any clues regarding this and therefore it is not that
| deep.
| stewbrew wrote:
| "model was trained on 84,743 retinal fundus photos from the UK
| Biobank dataset. External validation was performed on 252 fundus
| photos"
|
| Is it common practice in this field to test an overfitted model's
| performance with such a small data set so that the test could
| yield random results?
| hervature wrote:
| The probability of a 50% guess getting 75% accuracy (I believe
| the paper is something like 77.2%) on 252 trials is 1 in 10^15.
| drdeca wrote:
| Are you asserting that it is overfit?
|
| Also, before they tested on the other smaller dataset from a
| different source, aiui, they also trained only on the earlier
| subset of the first source, and used the later portion from the
| first source (with no overlap in patients) for the testing.
|
| (also, I'm not sure that 252 is really all that small?)
| make3 wrote:
| Why do you say that the model is overfitted? You have no way of
| knowing that. Plus, 84 743 is a very reasonable size for a
| vision dataset with a binary prediction
| avalys wrote:
| That was not the only validation set they used.
| kevingadd wrote:
| I would be curious whether this is actually classifying something
| that typically corresponds with sex, like hormone levels. In that
| case people with hormonal disorders would potentially be mis-
| classified, and someone photographed pre/during puberty might
| also be mis-classified. Since the paper mentions both neural and
| vascular tissue being represented in retinal photos, it seems
| like the levels of various hormones in the individual's blood
| could potentially also generate a mis-classification if they (for
| example) cause blood vessels in the eye to expand or contract.
| The mention that foveal pathology causes the model to mispredict
| suggests it would probably have issues in these cases too, I
| think.
|
| I wonder what actual values they were trying to predict with this
| analysis? Based on the paper, I get the impression they were
| trying to do something more interesting and they got the best
| data for sex.
| spuz wrote:
| They were specifically trying to classify sex because it is
| something that experts cannot already do:
|
| > While our deep learning model was specifcally designed for
| the task of sex prediction, we emphasize that this task has no
| inherent clinical utility. Instead, we aimed to demonstrate
| that AutoML could classify these images independent of salient
| retinal features being known to domain experts, that is, retina
| specialists cannot readily perform this task.
| cassonmars wrote:
| I wonder this too, and they could get a pretty solid answer if
| they incorporated transgender folks' data into the set since
| they're actively keeping their hormones in the desired ranges
| of their gender identity.
| esyir wrote:
| Generally you'd start with the common, easier problem before
| you delve into the abnormal cases. There are probably better
| ways to do that, like looking at bloodwork.
| lukeschlather wrote:
| That's fine for training, but for testing the model delving
| into abnormal cases seems required. If there's noticeable
| difference on abnormal cases that gives a lot of insight
| into what your model is actually testing.
| cassonmars wrote:
| All observations about the use of the term "abnormal"
| aside, I agree the idea of specifically isolating the
| hormone variable by using groups of people who naturally
| fit that range and groups of people that use medication
| to mirror that range would at least indicate whether this
| classifier is picking up on sexual dimorphism or vascular
| effects from hormonal differences (which also would
| potentially impact not only transgender people, but
| intersex people -- who make up around 3% of the
| population)
| chx wrote:
| They claim they are able to detect _gender_ which according to
| the relevant Canadian government website https://cihr-
| irsc.gc.ca/e/48642.html
|
| > Gender refers to the socially constructed roles, behaviours,
| expressions and identities of girls, women, boys, men, and gender
| diverse people
|
| First it's not some hamfisted mixup of sex and gender:
|
| > Terefore, this feld may contain a mixture of NHS recorded
| gender and self-reported gender. Genetic sex in the UK Biobank
| was determined
|
| And yet:
|
| > Predicting gender from fundus photos, previously inconceivable
| to those who spent their careers looking at retinas, also
| withstood external validation on an independent dataset of
| patients with different baseline demographics Although not likely
| to be clinically useful, this finding hints at the future
| potential of deep learning for the discovery of novel
| associations through unbiased modelling of high-dimensional data.
|
| If we had a way to detect trans children, for sure that would be
| clinically useful!
|
| Edit: as always, thanks for the downvotes, but please also
| educate me where I am wrong.
| phnofive wrote:
| The link points to some sort of viewer which lagged badly for me
| - here's the PDF:
|
| https://www.nature.com/articles/s41598-021-89743-x.pdf
| sxg wrote:
| (My mistake, missed their external validation)
| Isinlor wrote:
| They did external validation.
|
| > External validation was performed on the Moorfields dataset.
| This dataset differed from the UK Biobank development set with
| respect to both fundus camera used, and in sourcing from a
| pathology-rich population at a tertiary ophthalmic referral
| center. The resulting sensitivity, specificity, PPV and ACC
| were 83.9%, 72.2%, 78.2%, and 78.6% respectively
| fxtentacle wrote:
| Some women have 4 types of color rods, all men have only 3.
| galangalalgol wrote:
| Don't all women have 6 types but usually they all have very
| similar or even identical frequency response? Only when they
| have a colorblind gene are they noticeably different.
| fxtentacle wrote:
| I meant this one: https://en.wikipedia.org/wiki/Tetrachromacy
|
| "Tetrachromacy is the condition of possessing four
| independent channels for conveying color information, or
| possessing four types of cone cell in the eye."
|
| https://jov.arvojournals.org/article.aspx?articleid=2191517
|
| "12% of women are carriers of [..] anomalous trichromacy."
| galangalalgol wrote:
| Yes, it says it is from carrying a colorblind gene, usually
| red-green. But they have six copies of rhodopsin encoding
| dna, usuall 3 of them are duplicates. In red green
| colorblind only two are duplicates, but there are other
| types of colorblind. Theoretically they could be
| hexachromatic
| YeGoblynQueenne wrote:
| From the wikipedia page linked in your comment:
|
| _One study suggested that 15% of the world 's women might
| have the type of fourth cone whose sensitivity peak is
| between the standard red and green cones, giving,
| theoretically, a significant increase in color
| differentiation.[23] Another study suggests that as many as
| 50% of women and 8% of men may have four photopigments and
| corresponding increased chromatic discrimination compared
| to trichromats.[24]_
|
| It's not just women.
| wearywanderer wrote:
| Nor is color blindness exclusively a male phenomena; in
| Northern Europeans, 8% of males are colorblind while 0.5%
| of females are.
|
| Suppose a few more sexual dimorphic traits like this
| exist in the eyes; perhaps differences that have no
| practical effect on human vision and have consequently
| gone unnoticed by clinicians. If the ML model is picking
| up a few of these dimorphic traits, it could perhaps
| classify sex with more accuracy than anybody looking at a
| single trait could. This is pretty standard Bayesian
| stuff; it's the way basic "Plan for Spam" style Bayesian
| spam filters work.
| drcode wrote:
| Keep in mind ML models are really great at cheating to get
| answers: Maybe it's detecting that women have to tilt their head
| up more to reach the retinal photography machine, because they're
| shorter on average. Maybe, some of the images come from an
| optometrist that specializes in women's glass frames, and his
| retinal photography machine has a slightly dimmer bulb. Maybe,
| men are more likely to get a retinograph only when they have more
| severe disease already, so the retinas look different for that
| reason.
| max_ wrote:
| Thank you very much for this comment.
|
| My problem with modern scientific publications is that they
| focus more on the "discovery" instead of describing the logical
| rigor as to why their "discovery" could be true or false.
| ed25519FUUU wrote:
| Reproducibility is one thing I like about AI research. If they
| provide the model, I can take on my own computer and test it
| against whatever I want and judge it.
|
| Most things in science is almost impossible to reproduce
| because of cost or specialized equipment.
| Baeocystin wrote:
| To wit- the apocryphal Tank story:
|
| https://www.gwern.net/Tanks
| B1FF_PSUVM wrote:
| """
|
| Once upon a time--I've seen this story in several versions
| and several places, sometimes cited as fact, but I've never
| tracked down an original source--once upon a time, I say, the
| US Army wanted to use neural networks to automatically detect
| camouflaged enemy tanks.
|
| The researchers trained a neural net on 50 photos of
| camouflaged tanks amid trees, and 50 photos of trees without
| tanks. Using standard techniques for supervised learning, the
| researchers trained the neural network to a weighting that
| correctly loaded the training set--output "yes" for the 50
| photos of camouflaged tanks, and output "no" for the 50
| photos of forest.
|
| Now this did not prove, or even imply, that new examples
| would be classified correctly. The neural network might have
| "learned" 100 special cases that wouldn't generalize to new
| problems. Not, "camouflaged tanks versus forest", but just,
| "photo-1 positive, photo-2 negative, photo-3 negative,
| photo-4 positive..." But wisely, the researchers had
| originally taken 200 photos, 100 photos of tanks and 100
| photos of trees, and had used only half in the training set.
| The researchers ran the neural network on the remaining 100
| photos, and without further training the neural network
| classified all remaining photos correctly. Success confirmed!
|
| The researchers handed the finished work to the Pentagon,
| which soon handed it back, complaining that in their own
| tests the neural network did no better than chance at
| discriminating photos. It turned out that in the researchers'
| data set, photos of camouflaged tanks had been taken on
| cloudy days, while photos of plain forest had been taken on
| sunny days. The neural network had learned to distinguish
| cloudy days from sunny days, instead of distinguishing
| camouflaged tanks from empty forest.
|
| """
| sgt101 wrote:
| apocryphal ehh?
| X6S1x6Okd1st wrote:
| Yup. I learned this the hard way on the last model I trained at
| scale. It was evaluating fit between two heterogeneous classes
| I sampled training & test split off of a large time window and
| got to work. It performed extremely well on test & training.
| Too good.
|
| I pulled a third sample from a completely different time window
| and it performed terribly.
|
| It turned out that both datasets were dominated by class A
| being sorted into always selecting great fit or poor fit, so
| the ML model learned to memorize the class A instances.
|
| This problem when away when I subselected down to only
| instances of class A that had examples of both good fit and
| poor fit.
| acituan wrote:
| Their use of an external validation dataset eliminates many, if
| not all, of those concerns.
|
| Regarding external validation set:
|
| > This dataset differed from the UK Biobank development set
| with respect to both fundus camera used, and in sourcing from a
| pathology-rich population at a tertiary ophthalmic referral
| center.
|
| Regarding UK Biobank set (training set)
|
| > UK Biobank dataset, which is an observational study in the
| United Kingdom that began in 2006 and has recruited over
| 500,000 participants--85,262 of which received eye imaging38.
| Eye imaging was obtained at 6 centers in the UK and comprises
| over 10 terabytes of data39. Participants volunteered to
| provide data including other medical imaging, laboratory
| results, and detailed subjective questionnaires.
| nerdponx wrote:
| Still, until we have a better sense of what features the
| model is extracting, it's a surprising result and ought to be
| treated with caution.
| robocat wrote:
| Slight reflections of eyelashes? Especially if blurred?
| Kliment wrote:
| Here's a similar study from three years ago that tried to do the
| same with clinically relevant measures and got somewhat better
| results
| https://scihubtw.tw/https://www.nature.com/articles/s41551-0...
| fastaguy88 wrote:
| Sexy title, but it is unclear that clinicians can't classify sex
| from the retina, its just that they haven't bothered to. And the
| classification is not that great (<80% PPV on independent data).
| Clinicians will certainly get much higher sensitivity,
| specificity, and PPV just by looking at the subject ;)
| 988747 wrote:
| Clinicians don't care, so they never learn to distinguish. It's
| that simple.
| StreamBright wrote:
| ML model can't explain how it classifies retinal photographs,
| clinicians can.
| Causality1 wrote:
| Fascinating. I had no idea retinas were sexually dimorphic. I
| wonder if the difference serves a purpose or is just a
| consequence of some other adaptation.
| esyir wrote:
| There's also the risk of severe overfitting to some latent
| variable. I haven't quite dug into the work itself yet, but it
| does bring back memories of some case of perfect diagnosis due
| to hospital documentation process though.
| slibhb wrote:
| Neither did doctors apparently. If everything is kosher,
| they've proved some level of sexual dimorphism and now they can
| investigate and perhaps find out what it is.
|
| This is an interesting use of machine learning. We (or at least
| I) normally think of these models as replacing or complementing
| humans. But using them as a driver for research is cool.
| cerved wrote:
| This doesn't seem terribly well framed. Classifying the sex from
| a retinal photograph is useless. Obviously clinicians aren't
| going to be good at it. At which point I've lost interest
| spuz wrote:
| The paper is about 4 pages long - it takes about as long as it
| to you to write that comment as it does to skim through and
| learn that what you mentioned is exactly why they did the
| study:
|
| > While our deep learning model was specifcally designed for
| the task of sex prediction, we emphasize that this task has no
| inherent clinical utility. Instead, we aimed to demonstrate
| that AutoML could classify these images independent of salient
| retinal features being known to domain experts, that is, retina
| specialists cannot readily perform this task.
|
| It always amazes me how people spend 5 seconds reading a
| headline but think they know more than someone who has spent
| days and months on the same topic.
| cerved wrote:
| It's just an honest take. If I was more interested in the
| subject maybe I would skim more but I'm not interested
| enough.
|
| Not trying to shit on anyone, it's just a brutally honest
| opinion
| spuz wrote:
| Sorry I misinterpreted then. I thought you were dismissing
| it out of negativity but actually it's worse - you actually
| made a judgement that you knew more than the authors of the
| study.
| cerved wrote:
| The only judgement I made was to not read the whole
| paper. I read up until the paper stated that classifying
| sex based on retinal pictures was unlikely to be
| clinically useful. At which point I lost interest.
|
| Why wasn't the ML model and clinician classifying
| something that actually is clinically useful?
|
| If it has no clinical significance, what's the relevance
| of the classification of the clinicians?
|
| How is it any more spectacular than beating a random
| classifier?
|
| Had these points been addressed at this point I might
| have continued reading
| scarnak wrote:
| Why on earth are you even bothering to comment on the post
| then? If you're not interested enough to even skim the
| paper why do you think anyone would be interested to hear
| what your opinion of it is? You're not "brutally honest",
| you're just ignorant.
| cerved wrote:
| Because I had already spent time reading and maybe
| someone could enlighten me as to why it in fact is
| interesting. That and I was also hoping to get insulted
| Klinky wrote:
| I'd agree that if clinicians haven't been trained on this for
| their line work, then the comparison is not fair, but I
| wouldn't go so far as to say it's "useless".
| cerved wrote:
| They wrote in the paper it's useless
| Klinky wrote:
| Not having obvious clinical utility at the moment doesn't
| mean it's outright useless.
| cerved wrote:
| No, you're right. But since there's a whole field on the
| subject I figured they could have chosen something with
| clinical utility and I don't really understand why they
| didn't
| Scoundreller wrote:
| I remember a researcher doing some early research on compressing
| diagnostic imaging and was happy about all the hard disk space
| saved. They did some research to find out what level of
| compression they could go with that wouldn't result in different
| clinicians reaching different conclusions from the same images.
|
| It really upset me. We probably threw away decades of training
| data that a computer could have used for early detection.
|
| Fine for broken arms of whatever, but for cancer diagnostics,
| ugh. The computer might have been able to see the tumour before a
| clinician.
| [deleted]
| amelius wrote:
| What network topology did they use? I couldn't find it in the
| paper.
| kevinventullo wrote:
| In the section Model Training:
|
| "Our deep learning model was trained using code-free deep
| learning (CFDL) with the Google Cloud AutoML platform ... the
| CFDL platform provides the option of image upload via shell-
| scripting utilizing a .csv spreadsheet containing labels ...
| Automated machine learning was then employed, which entails
| neural architecture search and hyperparameter tuning."
|
| Earlier, in the Limitations section:
|
| "The design of the CFDL model was inherently opaque due to the
| framework's automated nature with respect to model architecture
| and hyperparameters. While this opacity is not unique to CFDL,
| there is potential to further reduce ML explainability due to
| lack of insight of model architectures and parameters
| employed."
|
| Maybe there's a whitepaper somewhere on how Google's AutoML
| works?
| maCDzP wrote:
| I am going to drop a thought here to see what happens. If there
| is a difference between male/female retinas. Could this affect
| our perception of reality?
| stirfish wrote:
| If it were to affect our perception of reality, what
| differences could we find?
|
| I'm guessing that it would affect our perception of reality in
| the same way eye color would.
| LeoPanthera wrote:
| Surely not any more than my terrible eyesight does. I don't
| think my spectacles are altering my perception of reality.
| Hoasi wrote:
| Mine certainly do, because without them I would be blind.
| [deleted]
| wearywanderer wrote:
| > _If there is a difference between male /female retinas_
|
| Is this even an "if"? It's well established that men are more
| likely to be colorblind, and it's likely many women are
| tetrachromats (most people are mere trichromats). The genes for
| the extra cone pigments are in the X chromosome, and are
| seemingly expressed more often when somebody has two X
| chromosomes. Similarly, people with two X chromosomes are less
| likely to be colorblind because most forms of colorblindness
| are caused by defects in genes in X chromosomes.
|
| https://en.wikipedia.org/wiki/Tetrachromacy#Humans
|
| https://en.wikipedia.org/wiki/Color_blindness#Genetics
|
| Perhaps most men and women, men and women with normal
| trichromatic vision, have identical retinas. But with genes so
| important to eyeballs residing in the X chromosome, who knows.
| But I'm left wondering why experts are particularly surprised
| by this result.
| BurningFrog wrote:
| My guess: Only in trivial ways.
|
| The fact that women see the world from a ~20cm lower point
| probably has real impact.
|
| For one thing, guys, your nose hair is very visible from that
| height.
| NaturalPhallacy wrote:
| A few funny anecdotes in that vein.
|
| A woman complained that her very tall boyfriend had hung a
| mirror in the bathroom. She took a picture of it. It was her
| reflection holding the camera level with the top of her head,
| and little else.
|
| One that happened to me personally. I asked my then gf (5'1")
| what it was like being a small person, do you feel like a
| normal sized person in a land of giants? "Yes" was the
| instant response.
|
| One very tall guy once remarked: "The tops of your fridges
| are fucking disgusting."
| xorfish wrote:
| > A woman complained that her very tall boyfriend had hung
| a mirror in the bathroom. She took a picture of it. It was
| her reflection holding the camera level with the top of her
| head, and little else.
|
| As a tall guy, there is a surprising number of bathroom
| mirrors where my reflection doesn't include my head.
| hervature wrote:
| I don't like philosophical questions like this. Let's say male
| blue is female red and vice versa. Our perception of the world
| is different yet it doesn't change anything as to how we
| understand and interact with "reality".
| vmception wrote:
| I would argue that it does and that we conform behaviors to a
| standard, but there are alot of assumptions we make that lead
| us to not understand each other at all
|
| There is a shared experience isolated to one sex that the
| other cannot perceive
| CyanBird wrote:
| It is well backed up by science that women can see or at
| least perceive/brainlog a stronger variety of colors than
| men, and then this is expressed on women having a stronger
| beefier vocabulary when it comes to naming and identifying
| colors
|
| So yeah, that's a thing
|
| Also, what you are describing is called Qualia, and that is
| intangible qualities of how the brain processes data, such as
| the "yellowness of a lemon", or the "foot pain of stepping on
| unexpected rock shoeless"
|
| Qualia can't be verbalized or compared between people because
| it is an inherent "brainfeel", you just need to expect others
| to have "at least similar-ish" qualias
| hervature wrote:
| Right, and children hear much higher frequencies than the
| rest of us. Just because you see more doesn't fundamentally
| change how we perceive reality. Like if someone says there
| is a color between eggshell white and snow white, I believe
| them because there is obviously a gradient there. I don't
| need to see their reality to agree on the state of it.
| bobthechef wrote:
| What's "fundamental" in this respect?
|
| If someone is colorblind, and another isn't, does that
| entail a change in perception? Sure. It means the
| colorblind guy can't discern things that people with
| normal vision can.
|
| A person born blind can't see anything and never has.
| They don't even imagine visual images (only images
| informed by the remaining senses). Their perception is
| unimaginable to me and mine to them.
|
| So if women can discern more colors than men, it follows
| that they experience more colors which seems like a
| matter of perception. Have you never argued with a woman
| about the color of a sweater?
| maCDzP wrote:
| Yeah - you are right. I thought about it and I guess since we
| can interact our perception can't be to far off - otherwise
| we wouldn't be able to procreate. It would be something out
| of the hitchhikers guide to the galaxy. A specie that because
| of a retinal differences between sexes is unable to mate.
| slver wrote:
| No
| matheuss-leonel wrote:
| Holy shit
| almog wrote:
| Just throwing a guess here about one factor that partitions
| photos based on sex might: height (I'm assuming males are
| generally taller).
|
| Looking at how a retinal photography machine looks like, I'd
| guess the height at which the photo is taken might slightly
| affect the POV angle, which in turn might be just enough to get
| caught by the ML model.
| hellbannedguy wrote:
| I had a Ph.D instructor in psychology.
|
| She said, women are actually taller than men when all cultures
| are studied.
|
| I don't care enough to dig deeper, but always stuck with me.
| jan_Inkepa wrote:
| That's an unexpected (to me at least) claim to make - https:/
| /onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.1330530... - I
| always thought of sexual dimoprhism (men taller than women on
| average) as a given and this paper backs up my claim
| specifically in the context of human societies (where it
| gives a 10cm height advantage in average in men over women,
| comparing 216 different societies ). There might be some way
| of counting in which the converse is true, but not in any way
| I know. I understand you don't want to dig deeper, but
| thought I'd flag it in case anyone else unwittingly digests
| this (possibly wrong) knowldge.
|
| https://www.quantamagazine.org/males-are-the-taller-sex-
| estr... is an example of a pop-sci article assuming the same
| basis (written by a biology phd).
| username90 wrote:
| People with Ph.D's often makes up facts and believes in
| nonsense just like everybody else.
|
| Edit: This includes those with STEM degrees as well. You
| really shouldn't trust someone more just because they have a
| research degree. I knew a professor who claimed to have
| solved some famous problems but that the peer reviewers just
| didn't want to accept that he solved it and therefore
| rejected his papers.
| make3 wrote:
| like you did just now. That person made something up. Let's
| not put all Ph.Ds in a bag just for that.
| Erik816 wrote:
| She appears to have made that up:
| https://www.worlddata.info/average-bodyheight.php
| sojournerc wrote:
| I'm curious if the a difference in cone density or distribution
| could be the differentiator.
|
| https://theneurosphere.com/2015/12/17/the-mystery-of-tetrach...
| DoubleDerper wrote:
| "EK is a consultant for Google Health. PAK has received speaker
| fees from Heidelberg Engineering, Topcon, Haag-Streit, Allergan,
| Novartis and Bayer. PAK has served on advisory boards for
| Novartis and Bayer, and is a consultant for DeepMind, Roche,
| Novartis and Apellis. KB has received research grants from
| Novartis, Bayer. Heidelberg and Roche. KB has received speaker
| fees from Novartis, Bayer, TopCon, Heidelberg, Allergan, Alimera.
| KB is a consultant for Novartis, Bayer and Roche. AK is a
| consultant to Aerie, Allergan, Novartis, Google Health, Reichert
| and Santen. All other co-authors have no competing interests to
| declare."
|
| Worth noting.
| mahathu wrote:
| This is a really impressive result and an interesting result to
| apply ML to. Thank you for sharing, OP. I'm just wondering if
| there are any real world applications of why you'd want to tell
| the sex of a person by a retinal photograph? It seems like a bit
| of a useless skill to have?
| qayxc wrote:
| I think this more an example how black-box models are basically
| useless for clinical research.
|
| The authors aren't aware of any distinguishing retinal features
| between male and female eyes and the model itself has no
| explanatory power.
|
| Could be a Clever Hans situation where the model exploits meta
| information of some kind in the absence of actual features. It
| could just as well mean that there are indeed distinguishing
| features that are compromised in the presence of foveal
| pathology.
|
| The authors note that another study using manually selected
| features identified three features that are indicative of
| genetical sex. These features yielded about 0.78 AUROC accuracy
| measure. Compared to the presented model's AUROC accuracy of
| 0.93 that's only 19% worse and these 19% additional accuracy
| may point to a combination of the already identified features
| or one or more additional features.
|
| I personally find this paper rather pointless. It stops at the
| point where actual progress could be made and things would get
| interesting - why didn't the authors evaluate the previously
| known features on the model's matches to measure their
| significance?
|
| This could have told them whether their black-box was relying
| on the same set of features as the ones identified by previous
| work, for example.
| zephyr____ wrote:
| This is a step backwards for LGBTQI rights.
|
| It is scientifically proven that there are no differences between
| men and women.
|
| I hope the inevitable retraction gets as much attention on HN.
| stirfish wrote:
| >It is scientifically proven that there are no differences
| between men and women.
|
| https://en.m.wikipedia.org/wiki/Sexual_dimorphism
| rs999gti wrote:
| > It is scientifically proven that there are no differences
| between men and women.
|
| XY and XX chromosomes don't matter? Then what are all those
| fertility doctors doing?
| claudiawerner wrote:
| Don't feed the troll.
| Traster wrote:
| >Clinicians are currently unaware of distinct retinal feature
| variations between males and females, highlighting the importance
| of model explainability for this task.
|
| If I'm reading this correctly what they're saying is that since
| we don't currently know the difference between male and female
| retinas, being able to explain what the ML black box is doing is
| important. But from what I can see in the paper they basically
| don't know what the black box is doing, they really don't
| understand what features their tool has isolated. I might be
| misunderstanding though?
| NieDzejkob wrote:
| Yes, they are highlighting the importance of research towards
| model explainability.
| slver wrote:
| I believe that's impossible.
| TaylorAlexander wrote:
| ML explainability is a wide field with a lot of success.
| For example you can discover what features are activating
| which detections. You comment, taken literally, suggests
| that research in this field is impossible. That is not the
| case.
| sigstoat wrote:
| when the activated feature in an image recognition net
| looks like a lovecraftian horror, that doesn't explain
| how the net came up with "turtle".
|
| explainability is going to have a rough time for the same
| reason ai alignment is going to have a rough time. people
| think they can explain decisions (technical and moral)
| far more effectively than they actually can.
| oogabooga123 wrote:
| Your question is confusing, it might be that you are using
| "feature" in an ML sense and the quote refers to human
| describable distinctions we know about? But I still don't know
| how to parse your question.
|
| The model can predict male vs female retinas but they don't
| understand why. What exactly are you asking?
| TaylorAlexander wrote:
| ML models have layers, and neurons in a given layer detect
| "features" in the image or in the previous layer. So yes I
| believe the person meant which structures in the image are
| activating the network. Which is a well studied area so it is
| surprising the authors didn't explore that.
| thaumasiotes wrote:
| I don't really understand your confusion?
|
| They say the following:
|
| - This model can distinguish photographs of a male retina from
| photographs of a female retina.
|
| - We don't know, ourselves, how to do that.
|
| - We would like to be able to determine, from looking at the
| model, what features it's using to draw the distinction.
|
| What's weird?
| mattkrause wrote:
| > We don't know, ourselves, how to do that.
|
| Have we actually tried?
|
| There is a cursory discussion about how this is
| "inconceivable to those who spent their careers looking at
| retinas". However, if it's not clinically useful (as the next
| sentence says), those experts probably haven't spent much--if
| any--training themselves to try.
|
| Humans can learn to detect surprisingly subtle features. For
| example, the right training regime can make you _much_ better
| are reporting a tile of a line, but it requires practice and
| feedback, just like the network got.
| Traster wrote:
| What I'm confused by is that they say this is important to
| do, but then don't actually seem to do it?
| JabavuAdams wrote:
| Important to do next.
| make3 wrote:
| future work is almost always discussed in publications
| joe_the_user wrote:
| They don't give an explanation because they don't know how
| to give an explanation - many if not most ML model lack an
| easy explanation presently, they just spit out answers.
|
| They are saying "someone should do this because it's
| important even though we don't (presently) know how to do
| this".
| aaron-santos wrote:
| I'm open to learning why class activation maps (CAM)
| would or wouldn't be a good place to start.
| hervature wrote:
| We're not at the point of explaining complicated models with a
| straight face. Based on the saliency maps, it looks like the
| model has learned something around the bright circles (or is it
| the blind spot? Not an ophthalmologist). Makes me think the
| network can reverse engineer distortions in the light to get
| curvature of the lens which might be indicative of gender
| differences.
| contrarian_5 wrote:
| thats going to be one of the biggest shocks to society going
| forward when it comes to the changes that AI bring. there are
| mountains of data everywhere that is completely overlooked simply
| because the cost of processing the data is too high. too high to
| discover patterns/correlation and too high to process in any
| case.
|
| human beings filter out most of what goes on around them. they
| dont see the world as it is and their minds dont keep track of
| physical primitives. their minds abstract the world into larger
| conceptual parts and track those parts. its not just a question
| of processing power, its a question of intuitive access. and
| nobody realizes this yet because the only sentient beings who are
| around to demonstrate any of this have those filters in place.
| when the AI comes with all that horse power and with no filters,
| it will see things all around that we are blind to. it will seem
| as though it can make impossible predictions. it will seem god-
| like, even before it graduates to doing something other than
| simply observing the world.
| wizzwizz4 wrote:
| Odds on it detecting mascara or eyelashes or some other makeup?
| Retinal photos have to go through the front of the eye, after
| all.
| terramex wrote:
| If there ever was mascara on your retina you better be going to
| the hospital quickly.
| [deleted]
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