Computer methods and programs in biomedicine · 2026
Intrinsic dimension for evaluating and analyzing neural networks for medical image segmentation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Wutong, Li Zeju, Xu Jie, Zhou Shichong, Guo Yi, Tao Qian, Wang Yuanyuan
Аннотация
BACKGROUND AND OBJECTIVE: Evaluating medical image segmentation models typically relies on expert annotations, which are scarce, costly, and time-consuming to obtain. Intrinsic dimension (ID), defined as the minimal number of parameters required to describe high-dimensional data, has been shown to correlate with model generalization in classification tasks, but whether this relationship extends to segmentation remains unclear.
METHODS: In this study, we systematically investigate ID as a label-free indicator for evaluating segmentation performance, including the generalization of standard segmentation models and the adaptability of promptable foundation models (i.e., SAM-based models). We also perform a layer-wise ID analysis to characterize variations in latent representation complexity. Furthermore, we propose a novel, computation- and memory-efficient strategy for ID calculation to broaden its applications.
RESULTS: We reveal the following findings: 1) For standard segmentation, lower ID values of training-sample representations are consistently associated with better generalization performance on unseen test samples, as measured by DSC. 2) For promptable segmentation, test-data ID consistently reflects model adaptability to specific target data compared to entropy, a metric commonly believed to predict performance. 3) Layer-wise ID in segmentation follows an initially increasing and then decreasing pattern, similar to that in classification.
CONCLUSIONS: Our findings suggest the potential of ID as a label-free indicator for assessing the generalization and adaptability of segmentation models, thereby supporting model selection in label-scarce clinical settings.
METHODS: In this study, we systematically investigate ID as a label-free indicator for evaluating segmentation performance, including the generalization of standard segmentation models and the adaptability of promptable foundation models (i.e., SAM-based models). We also perform a layer-wise ID analysis to characterize variations in latent representation complexity. Furthermore, we propose a novel, computation- and memory-efficient strategy for ID calculation to broaden its applications.
RESULTS: We reveal the following findings: 1) For standard segmentation, lower ID values of training-sample representations are consistently associated with better generalization performance on unseen test samples, as measured by DSC. 2) For promptable segmentation, test-data ID consistently reflects model adaptability to specific target data compared to entropy, a metric commonly believed to predict performance. 3) Layer-wise ID in segmentation follows an initially increasing and then decreasing pattern, similar to that in classification.
CONCLUSIONS: Our findings suggest the potential of ID as a label-free indicator for assessing the generalization and adaptability of segmentation models, thereby supporting model selection in label-scarce clinical settings.