This loss function comprises two components: exponential cross-entropy and label smoothing. The exponential cross-entropy component applies a strong penalty to misclassified samples, thereby ...

While softmax cross-entropy (CE) loss is the standard objective for supervised classification, it primarily focuses on the ground-truth classes, ignoring the relationships between the nontarget, ...

The model was trained with the Adam optimizer and binary cross-entropy loss function. Early stopping was applied after 5 epochs of no improvement, with the best model weights saved using checkpointing ...

Self-supervised deep learning models can accurately perform 3D segmentation of cell nuclei in complex biological tissues, enabling scalable analysis in settings with limited or no ground truth ...