The day in conversation for 2026-10-21

The discussion: Rosa: The core strength of PHAST-Net seems to be how it forces different representations—the wavelet transform and those high-res spectrograms—to share a common latent space before applying the physics loss, but I’m still curious if that attention mechanism is actually suppressing cross-terms effectively across those vastly different dimensional scales. Dev: From my perspective on the network architecture, that unified space is crucial because it allows us to treat time and frequency as coupled variables rather than separate domains; but what specific metrics are you using to quantify that suppression when comparing the output spectrograms against a standard learned representation? Taro: That quantification is tricky because OncoReg deals with spatial registration where the error tolerance is much tighter, focusing on voxel-level alignment between cone-beam and