Towards Efficient and Accurate CT Segmentation via Edge-Preserving Probabilistic Downsampling

EMANTIC segmentation plays a pivotal role in medical image analysis by differentiating organs and anatomical or introducing Gaussian noise can create uncertainty around structures by assigning a definitive class to each pixel, producing object boundaries. Furthermore, soft labels can stem from hard labels. Despite recent advancements that benefit annotators' disagreements regarding object boundaries in intraand from large datasets and significant computational power, such inter-rater annotations [11]. Averaging or fusing such dependencies pose challenges for researchers with constrained annotations produces soft labels, while the majority voting budgets. The necessity for full-resolution image processing makes hard labels. Empirically, loss functions tend to steer demands considerable computational resources and memory, network predictions towards extreme values (0 or 1) rather limiting broader participation. In response, lightweight networks than closely aligning with target soft labels, affecting class with fewer trainable parameters have been proposed, probability estimations, learning trajectories, and performance facilitating operation on mid-to low-range devices at the metrics.