CODEWITHSUNDEEP
deep-learningcomputer-visionconv-neural-network
Odgiiv
Odgiiv

Reputation: 713

How to make CNN learn positional constraints?

I am working on image segmentation problem in medical domain using fully connected CNN.

The problem is that for particular image, it could have a lot of similar structures. Our task is to find the correct one. One thing that I'd like to make the CNN learn is that there should not be a structure below another structure which is found first on the top. In the ground truth images, it is implicitly shown because there is only one structure in each image. Is it possible to achieve it with CNN? If not, what could be done to achieve it?

Upvotes: 3

Views: 291

Answers (1)

dr_m7
dr_m7

Reputation: 86

With a traditional CNN, positional constraints cannot be learned, because the learning all takes place in convolutional layers which are spatially invariant. One caveat to this is that a CNN will learn relative arrangements of features to a certain extent (if feature A is always above feature B, successful classification of pixels belonging to A will implicitly decrease the likelihood of pixels above being classified as B, at least for pixels that are "sufficiently close", because the boundary region will be the opposite of what the CNN has been trained on). If you do not consider that sufficient, you would need to either design a custom layer that somehow considers position (although if there is only one structure in each ground truth image I'm not sure your data is sufficient to teach anything about relative locations of multiple objects as-is beyond the aforementioned caveat) or just post-process the CNN output with a non-learning algorithm that is designed based on your expert knowledge of these positional constraints. As a fellow medical computer vision engineer, I would recommend the latter, especially since it sounds like you are dealing with a hard no-exceptions rule (why bother trying to learn a rule that is already simple?).

Upvotes: 3

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