moreshud:

Firstly, is the annotation or labeling of the background necessary for the performance of the model since it will be dropped during prediction or inference?


The background class would be necessary, if your model is supposed to predict the class of each pixel.
During training you could ignore the background class pixels, but then your model won’t be able to predict the background during evaluation and the predicted segmentation would contain only the two valid classes.



 moreshud:

Secondly, due to the highly imbalance nature of the dataset, suggest approaches as read on the forum is either to use weighted sampler or weighted loss function. As my preferred choice of weighted dice loss function, how will I compute the weight for each class (suggested approaches are targeted at classification task not segmentation)?


I’m unsure if weighted sampling would work nicely for segmentation use cases, since each sample would most likely contain more than a single class. Drawing a particular sample would thus increase the class count of multiple classes and you would end up with the limitations for a multi-label classification use case.



 moreshud:

Finally, what is the significance of a combine loss function i.e dice loss and cross-entropy loss function?


Both losses will be used to calculate the gradients and each parameter will accumulate the gradient from them.