Hi all,
Iam working on a segmentation project, where the input are CT images and the output consists of segmentations with a total of 3 classes.
My first model was trained by transforming the segmentation map into a one-hot encoded vector and applying a softmax after the final layer. (so actually 4 class labels when counting the background)
That works quite ok, but I have noticed that labels can overlap in certain cases.
My idea was therefore to stack the 3 different segmentation maps into 3 seperate image channels, where each segmentation is binary (background = 0, foreground = 1).
To train the model I used the BCEwithLogits loss and during training the result looks good.
Nevertheless, when using the model to predict new segmentations for a given CT image, the model outputs the same segmentation in all three channels and I can’t figure out whats wrong…
(Just to be sure that Iam not providing wrong target segmentations,  I already checked those…)
Any ideas or hints would be awesome!
Thanks in advance,
Cheers,
Michael