In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final *** clinical routine helps to mitigate individual ***,when data is annotated by multiple experts,standard...
详细信息
In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final *** clinical routine helps to mitigate individual ***,when data is annotated by multiple experts,standard deep learning models are often not *** this paper,we propose a novel neural network framework called Multi-rater Prism(MrPrism)to learn medical image segmentation from multiple *** by iterative half-quadratic optimization,MrPrism combines the task of assigning multi-rater confidences and calibrated segmentation in a recurrent *** this process,MrPrism learns inter-observer variability while taking into account the image's semantic properties and finally converges to a self-calibrated segmentation result reflecting inter-observer ***,we propose Converging Prism(ConP)and Diverging Prism(DivP)to iteratively process the two *** learns calibrated segmentation based on multi-rater confidence maps estimated by DivP,and DivP generates multi-rater confidence maps based on segmentation masks estimated by *** results show that the two tasks can mutually improve each other through this recurrent *** final converged segmentation result of MrPrism outperforms state-of-the-art(SOTA)methods for a wide range of medical image segmentation *** code is available at https://github.-com/WuJunde/MrPrism.
Non-negative matrix factorization (NMF) is a classical data analysis tool for clustering tasks. It usually considers the squared loss to measure the reconstruction error, thus it is sensitive to the presence of outlie...
详细信息
Non-negative matrix factorization (NMF) is a classical data analysis tool for clustering tasks. It usually considers the squared loss to measure the reconstruction error, thus it is sensitive to the presence of outliers. Looking into the literature, most of the existing robust NMF models focus on statistics-based robust estimators with known distribution assumptions. Besides those estimators, whether can we seek another function without the distribution assumption to boost the robustness of NMF? To solve the problem, we propose a robust NMF termed as tanh NMF for short, which rethinks the hyperbolic tangent (tanh) function as a robust loss to evaluate the reconstruction error. Moreover, to capture geometric structure within the data, we devise a locality constraint to regularize tanh NMF to model data locality. Owing to the non-convex tanh function, it is non-trivial to optimize tanh NMF. Following the paradigm of the half-quadratic algorithm, we easily solve an adaptive weighted NMF instead of original tanh NMF. The experiments of face clustering on four popular facial datasets with/without corruptions show that the proposed method achieves the satisfactory performance against several representative baselines including NMF and its robust counterparts. This also implies that the proposed tanh function could serve as an alternative robust loss for NMF.
暂无评论