Shortcut learning is a phenomenon where machine learning models prioritize
learning simple, potentially misleading cues from data that do not generalize
well beyond the training set. While existing research primarily investigates
this in the realm of image classification, this study extends the exploration
of shortcut learning into medical image segmentation. We demonstrate that
clinical annotations such as calipers, and the combination of zero-padded
convolutions and center-cropped training sets in the dataset can inadvertently
serve as shortcuts, impacting segmentation accuracy. We identify and evaluate
the shortcut learning on two different but common medical image segmentation
tasks. In addition, we suggest strategies to mitigate the influence of shortcut
learning and improve the generalizability of the segmentation models. By
uncovering the presence and implications of shortcuts in medical image
segmentation, we provide insights and methodologies for evaluating and
overcoming this pervasive challenge and call for attention in the community for
shortcuts in segmentation.

研究通过探索临床注释以及数据集中的零补齐卷积和中心裁剪训练集等因素如何无意中成为快捷方式对医学图像分割准确性产生影响，并确定并评估了两个常见的医学图像分割任务中的快捷方式学习。此外，提出了减轻快捷方式学习影响、提高分割模型普适性的策略，并为评估和克服医学图像分割中普遍存在的挑战提供了见解和方法。