Uncertainty estimation for trust attribution to speed-of-sound reconstruction with variational networks [0.03%]
基于变分网络的速度重建可信度评估及不确定性估计
Sonia Laguna,Lin Zhang,Can Deniz Bezek et al.
Sonia Laguna et al.
We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference....Conclusion: A novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.
Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift [0.03%]
crowdsourcing标注的软标签可以改善分布变化下的不确定性估计
Dustin Wright,Isabelle Augenstein
Dustin Wright
We demonstrate that this yields classifiers with improved predictive uncertainty estimation in most settings while maintaining consistent raw performance compared to learning from individual soft-labeling methods or taking a majority vote of the annotations....We additionally highlight that in regimes with abundant or minimal training data, the selection of soft labeling method is less important, while for highly subjective labels and moderate amounts of training data, aggregation yields significant improvements in uncertainty estimation over individual methods
Evaluation of uncertainty estimation methods in medical image segmentation: Exploring the usage of uncertainty in clinical deployment [0.03%]
医学图像分割中不确定性估计方法的评估：探索不确定性在临床应用中的使用价值
Shiman Li,Mingzhi Yuan,Xiaokun Dai et al.
Shiman Li et al.
Despite their significance, the adoption of uncertainty estimation methods in clinical practice remains limited due to the lack of a comprehensive evaluation framework tailored to their clinical usage....Using this systematic evaluation framework, five mainstream uncertainty estimation methods are compared on organ and tumor datasets, providing new insights into their clinical applicability. Extensive experimental analyses validated the practicality and effectiveness of the proposed metrics....This study offers clear guidance for selecting appropriate uncertainty estimation methods in clinical settings, facilitating their integration into clinical workflows and ultimately improving diagnostic efficiency and patient outcomes.
SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation [0.03%]
基于可解释的可扩展集成进行不确定性估计的分段和综合（SASWISE-UE）
Weijie Chen,Alan B McMillan
Weijie Chen
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables en...
J E Sobczyk,W Jiang,A Roggero
J E Sobczyk
Crucially, the method allows for a robust uncertainty estimation of the spectral reconstruction. We employ it to obtain the spin response in neutron matter.
A transformation uncertainty and multi-scale contrastive learning-based semi-supervised segmentation method for oral cavity-derived cancer [0.03%]
一种用于口腔癌变的转换不确定性及多尺度对比学习半监督分割方法
Ran Wang,Chengqi Lyu,Lvfeng Yu
Ran Wang
The transformation uncertainty estimation evaluates the model's confidence on data transformed via different methods, reducing discrepancies between the teacher and student models.
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior [0.03%]
基于R2D2先验的贝叶斯神经网络保真收缩方法研究
Tsai Hor Chan,Dora Yan Zhang,Guosheng Yin et al.
Tsai Hor Chan et al.
Experiments on both natural and medical image classification and uncertainty estimation tasks demonstrate satisfactory performances of our method.
FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17 [0.03%]
融合注意力机制和不确定性估计的肝肿瘤分割深度学习模型FasNet在LiTS17上的研究
Rahul Singh,Sheifali Gupta,Ahmad Almogren et al.
Rahul Singh et al.
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional ...
Towards ROXAS AI: automatic multi-species ring boundaries segmentation as regression in anatomical images [0.03%]
ROXAS AI：解剖图像中多物种环状边界分割的回归方法研究
Marc Katzenmaier,Vivien Sainte Fare Garnot,Jan Dirk Wegner et al.
Marc Katzenmaier et al.
The newly added uncertainty estimation of our method allows for faster and more targeted validation of our results, saving a large amount of human labor.
Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology [0.03%]
基于组学不确定性估计的深度高斯过程微卫星不稳定型和免疫治疗反应预测研究
Sunho Park,Morgan F Pettigrew,Yoon Jin Cha et al.
Sunho Park et al.
Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune checkpoint inhibitors (ICIs) and oftentimes no...
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