Hi @tom thanks for your comment!
So the model I am working on is training well with a soft dice loss ~0.10 (approximate dice of 0.90). I am formulating it as a 3d problem, so there isn’t the notion of oversampling the individual images (or "slices) but the data is similar to pulmonary nodules in some sense and only represents a fraction of the entire volume.
What’s interesting to me is that most people apply a threshold on a probability distribution but it seems like we could perhaps do significantly better? It’s usually apparent where the correct segmentation is from the “shape” and appearance of features in the probability distribution to some extent. Thresholds can naively miss this information?