\begin{table}[H]
\centering
\caption{Implementation details used for training ICL-NoiseUNet.}
\begin{tabular}{ll}
\hline
\textbf{Component} & \textbf{Setting} \\
\hline
Framework & PyTorch Lightning \\
Optimizer & AdamW \\
Learning rate & $1 \times 10^{-5}$ \\
Weight decay & $1 \times 10^{-7}$ \\
Batch size & $4$ ($1$ target + $4$ context samples) \\
Epochs & $50$ \\
Early stopping & Patience = $8$ epochs (validation loss) \\
Loss function & Dice Loss \\
Context size (train) & $L = 4$ (random sampling) \\
Context selection (inference) & $4$ images with the minimum L2 distance\\
Noise map window size & $7 \times 7$ \\
Augmentation (targets only) & Flips, rotation, elastic deformation, zoom,\\
& Gaussian noise, brightness/contrast \\
Context augmentation & None \\
Dataset split & $70$ / $15$ / $15$ (train/validation/test) \\
GPU & NVIDIA GeForce GTX~1080 (1 GPU)\\
Checkpointing & Best model via validation loss \\
\hline
\end{tabular}
\end{table}
