Fig. 6: CellMincer hyperparameter settings and their resulting models’ performance on Optosynth data.

Each model was evaluated on both its training data (b, e) and unseen test data (c, f). a–c Initial series of experiments using no global features as a baseline. d–f Followup iteration of experiments using repeated global features as a baseline. The global features setting determines whether the precomputed global features is not used (0), used to augment the U-Net input only at the beginning (1), or used to augment repeatedly at every contracting path step (R). The included temporal post-processor variants refers to the architecture of the ultimate multilayer perceptron component: C → C/2 → C (A), C → C → C/2 → 1 (B), and C → C → C → 1 (C). The architectural variants are ordered in increasing complexity. The pixel masking setting refers to the Bernoulli parameter used to decide whether each pixel is masked, a sampling process repeated for each training iteration. The second set of experiments adjusts the original baseline model to use a conditional U-Net with repeated global features.