[jjallen]

Very cool. I learned a lot as a non dermatologist but someone with a sister who has had melanoma at a very young age.

I went from 50% to 85% very quickly. And that’s because most of them are skin cancer and that was easy to learn.

So my only advice would be to make closer to 50% actually skin cancer.

Although maybe you want to focus on the bad ones and get people to learn those more.

This was way harder than I thought this detection would be. Makes me want to go to a dermatologist.

[sungam]

Thanks, this is a good point - I think a 50:50 balance of cancer versus harmless lesions would be better and will change this in a future version.

Of course in reality the vast majority of skin lesions and moles are harmless and the challenge is identifying those that are not and I think that even a short period of focused training like this can help the average person to identify a concerning lesion.

[wizzwizz4]

https://xkcd.com/2501/

[alanfranz]

> So my only advice would be to make closer to 50% actually skin cancer.

If I were to code this for "real training" of a dermatologist, I'd make this closer to "real world" training rate. As a dermatologist, I'll imagine that probably just 1 out of 100 (or something like that) skin lesions that people could imagine are cancerous, actually are so.

With the current dataset, there're just too many cancerous images. This makes it kind of easy to just flag something as "cancerous" and still retain a good "score" - but the point is moot, if as a dermatologist you send _too many_ people without cancer to do further exams, then you're negating the usefulness of what you're doing.

[mewpmewp2]

It needs a specific scoring system where each false positive has a lower score drop, but false negative has a huge one. At the same time like you said positives would be much rarer. Should be easy to ask LLM to vibe code that so it would simulate real world and its consequences.

[jjallen]

Thought about this some more. I think you want to start at 100% or high so people actually learn what needs to be learned: what malignant skin conditions actually look like.

And then once they have learned you get progressively harder and harder. Basically the closer to 50% you are the harder it will be to have a score higher than chance/50%.

[loeg]

I found the first dozen to be mostly cancer and then the next dozen were mostly non-cancer. (Not sure if it's randomized.) (Also, I'm really bad at identifying cancerous vs non-cancerous skin lesions.)

[sungam]

It is randomized so probably just bad luck! FWIW I get a high score and another skin cancer doctor who commented also gets a high score so it is possible to make the diagnosis in most cases on the basis of these images.