Propose an architecture for your convolutional layer that adaptively corrects for differences in image statistics across different locations, justifying the tradeoff with the model’s statistical efficiency.

Implement a convolutional layer where each location learns its own bias. This allows the model to adapt to varying image properties and background noise at different locations, trading off slightly increased computation time and memory usage for improved accuracy in medical image analysis tasks.