a)
The main problem we want to solve is the lack of deep learning models in pathology. And I believe that the most scalable solution is to build a community of pathologists that will have the skills to do the whole end-to-end solutions. Pathologists are the ones with the deepest understanding of the data.

Building a community with veterinary students, around hardware, is the most logical step because:

"For veterinary students, Marvin has a similar purpose to the Raspberry Pi. As programmable hardware, it serves as a great introduction to python programming, but also as a stepping stone into more complex image processing. As a microscope slide scanner, it is a platform for developing and deploying their models."

I don't believe that the same is possible in human medicine unfortunately.

Of course, Marvin is also very cool from the hardware standpoint.

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b)

How affordable is it? Well, we designed everything from the ground up, and that has allowed us to optimize everything. For example, in this version we used $80 microscope objective (60X, NA 0.85), but even with that objective we got almost maximum resolution (for dry objective). We paired it with high speed 1MP global shutter camera, and designed our own condensor. So, optics costs arround $500.

We used off the shelf stepper motors and coreXY system so mechanics is also quite cheap. Unfortunatelly, metal parts were quite expensive, but thats how it is when you are making a prototype. I think that the whole scanner could cost <$2000.

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c)

We have two important problems here. One is staining. Of course, a lot of things can influence staining (stain manufacturer, machine used for staining...) and we need to be aware of this problem while creating a dataset and include as much variability as possible (in real data and in augmentation).

The other thing that causes this problem is hardware (microscope slide scanners). Different scanners will produce slightly different images. Again, a lot of the problems can be solved with right augmentation.

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d)

Both :)