I am implementing a keypoint detection algorithm to recognize biomedical landmarks on images. I only have one type of landmark to detect. But in a single image, 1-10 of these landmarks can be present. I'm wondering what's the best way to organize the ground truth to maximize learning.
I considered creating 10 landmark coordinates per image and associate them with flags that are either 0 (not present) or 1 (present). But this doesn't seem ideal. Since the multiple landmarks in a single picture are actually the same type of biomedical element, the neural network shouldn't be trying to learn them as separate entities.
Any suggestions?
One landmark that can appear everywhere sounds like a typical CNN problem. Your CNN filters should learn which features make up the landmark, but they don't care where it appears. That would be the responsibility of the next layers. Hence, for training the CNN layers you can use a monochrome image as the target: 1 is "landmark at this pixel", 0 if not.
The next layers are basically processing the CNN-detected features. To train those, your ground truth should be basically the desired outcome. Do you just need a binary output (count>0)? A somewhat accurate estimate of the count? Coordinates? Orientation? NN's don't care that much what they learn, so just give it in training what it should produce in inference.