HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg
When you were calculating F1, did you use the algorithm provided by the GlaS competition official website to calculate each object?
That is, is your calculation the same as instance segmentation?
Or,
is your calculation in f1 score is calculated by pixel?
I want to ask a question.
When you were calculating F1, did you use the algorithm provided by the GlaS competition official website to calculate each object? That is, is your calculation the same as instance segmentation?
Or, is your calculation in f1 score is calculated by pixel?
Thank you.