Hello, I am working with a Unet segmentation model for medical image analysis, and I would like to know how important are negative examples (with empty masks) for my model to learn that some images are 100% negatives. I am asking this because I took a bunch of negative and added to my dataset, some kind of hard negative mining, and still, I am getting a lot of false positives. Does Unet learn anything with negative examples? Is there any other way to force my model to learn these ‘negative’ features?
My dataset is very skewed, I am working with medical image and I want to segment little specimens in 256x256 patches. The number of negative patches (no specimen) and the number of positive patches are balanced but the number of positive positive pixels is 1-2%.
I read that weighted sigmoid cross-entropy might be a good idea to attenuate this difference and get a lower rate of false positives.
I would like to get your opinion
If you could also provide relevant information about this (articles, papers, questions) I would highly appreciate.