Purpose:  Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities.

Mario Kart has taught us to expect a close-up, over-the-shoulder view of our arcade racers, but it didn't always used to be this way. Back in the NES days, the likes of R.C. Pro-Am and Super Off-Road offered a birds-eye view of the action.


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It's this old-school racing spirit that developer 21c. Ducks seeks to evoke with Super Pixel Racers. Looking down on your pixellated rally car, like a drone covering the World Rally Championship, there's an instant warm and fuzzy retro feel to the action. While the cars you'll racing are all pretty grounded approximations of classic rally cars (Subarus and Mitsubishis, you know the drill), this is anything but a racing simulator. Your car auto-accelerates for one thing, while using a drift button to slide around corners will accrue boost for your nitro.

The developer recommends that you use a control method whereby your car steers in whatever direction the left Joy-Con stick is pointing, but we found this difficult to get our heads around. Far better, to our mind, is the alternative 'Classic' method, whereby left and right on the stick will rotate your car anti-clockwise and clockwise respectively. It's the method top-down racers used to take, and it works a treat here.

Special mention should go to Super Pixel Racers' presentation. There's nothing outrageous here from a technical perspective, but we're fans of the 16-bit pixel art style. Some might find the rudimentary animation a little too crude, but it takes us back to an earlier, more innocent time of arcade racing. The audio, too, is excellent, with the kind of driving synth soundtrack and high-pitched engine noises that speak to the classic era of top-down racers it's trying to emulate.

If we're being frank, both of these top-down racers are flat out better, richer games than Super Pixel Racers. That said, you might just warm to the 16-bit charm of Super Pixel Racers a little more. Each offers enough of a distinctive experience in terms of handling that you'd be more than justified taking all three for a spin.

Although there are a lot of pixel games now and I (to a point) understand the frustration, I think 2D scrolling games are the real flooded market game-type at this point - and so anyone producing good top-down, isometric or 3D games still get extra points from me.

@FullMetalJumper he literally mentioned that in the review. Mantis Burn and Gotcha Racing was the other one. I've got Mantis Burn but I found it a bit dull. I'm going to save my moolah for Burnout Paradise, even though I'd rather not pay big cash for a friggin open world racer. What can I say, I'm a sucker for Burnout.

@FullMetalJumper Paradise looks near-identical to a game I had on Wii U, Need For Speed Most Wanted. While I enjoyed that game the open world format makes it a bit aimless - I just want to jump into a race. Mario Kart aside my favourite racer on Switch has been Horizon Chase Turbo. Super Pixel Racers has definitely piqued my interest though. I love how many different modes it has. Would love a demo.

@HXLXIII So he's an ass for expressing opinion is he? No mate you are. He's not wrong, the eshop is flooded with gash pixel games. Take your rose tinted specs off. There's very very few pixel art games out today that are any good. We all grew up with them and have a place in our hearts but come on

Just like the patent race in the pharmaceutical industry, there is what we call the pixel race in the electronics industry. Every manufacturer that uses visualisation technology (CCD, CMOS, OLED, LCD, ...) to produce broadcast camera's, digital photo camera's, TV's, tablets, ... tries to push us as consumer and professional to go to more pixels because of so-called better image quality. It started with the change early years 2000 from analog TV to digital TV.

- picture quality is not only a matter of pixels, in the early days of 1M pixel camera's it was the case, but today you can easily say that anything above 22M pixel is a luxury ; picture quality is more about mastering light, taking a picture is much more than just a click

The TV broadcast industry is by far leading the pixel race, the technology then finds its way down to other industries such as the projector industry, the pc industry, the CCTV industry, ... . But all these industries are linked, because you can take a 100M pixel picture and show it on your HD-ready display ? Makes no sense, right !

I believe the pixel race will come to an end and stay at the 4K level for the next decade. Let's be honest what comes next after the 100M pixel camera ? A Giga pixel camera ? How many razor blades does a razor need to shave perfectly ?

I hope also to address the large companies who make professional camera's - not to mention names - to address the professional market with better advice and support. For many years the focus for them was on achieving the highest marketshare in the consumer (volume) market, making commodity (click-and-go) camera's. I personally believe the future for most of these companies is with the professional and they should spend more time with the professional photographer to develop new camera's (and not just walk the pixel line).

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Chris Tignanelli, MD, MS, recently collaborated with a group of researchers to determine if artificial intelligence (AI) can predict self-reported race from chest x-ray images by using pixel intensity counts.

Researchers are not sure how these models predict self-reported race as there are no known features in medical images that can be used to determine race. Dr. Tignanelli and his colleagues wanted to know if AI models are potentially using the grayscale values of image pixels to make their predictions.

To answer this question, the researchers counted the number of pixels of each grayscale value, or pixel intensity, in a large dataset of chest x-rays. They then performed a statistical test to see if different racial groups have different pixel intensity counts and trained machine learning models to predict self-reported race based on these counts.

Overall, the researchers were not sure if the AI models developed in previous studies used pixel intensity to predict self-reported race. However, this research demonstrates that race is embedded in medical images in ways that are not always apparent to human observers.

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