The binary cross entropy function is assuming there are sigmoid activations, so you should apply a sigmoid to the logits to recover the probabilities. However those probabilities are likely to be poorly calibrated, and so not actually be reflective of the true rate of a particular class (i.e. if it predicts the positive class with 90% probability that doesn’t mean that 9 times out of 10 it will be positive). If you really need high quality probability estimates I’d recommend looking at calibration, or maybe even using something like Bayesian deep learning.