Results 311 to 320 of about 1,558,397 (364)

Dice

Proceedings of the VLDB Endowment, 2021
In order to conduct analytical tasks, data scientists often need to find relevant data from an avalanche of sources (e.g., data lakes, large organizational databases). This effort is typically made in an ad hoc, non-systematic manner, which makes it a daunting endeavour.
El Kindi Rezig, Anshul Bhandari, Anna Fariha, Benjamin Price, Allan Vanterpool, Vijay Gadepally, Michael Stonebraker   +6 more
semanticscholar   +3 more sources

Probabilistic linguistic multiple attribute group decision making for location planning of electric vehicle charging stations based on the generalized Dice similarity measures

Artificial Intelligence Review, 2021
Gui-Wu Wei, Cun Wei, Jiang Wu
exaly   +2 more sources

DICE: Leveraging Sparsification for Out-of-Distribution Detection

European Conference on Computer Vision, 2021
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking ...
Yiyou Sun, Yixuan Li
semanticscholar   +1 more source

DICE

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2009
One of the core challenges now facing smart rooms is supporting realistic, everyday activities. While much research has been done to push forward the frontiers of novel interaction techniques, we argue that technology geared toward widespread adoption requires a design approach that emphasizes straightforward configuration and control, as well as ...
Gene Golovchinsky, Pernilla Qvarfordt, Bill van Melle, Scott Carter, Tony Dunnigan   +4 more
  +4 more sources

Rethinking Dice Loss for Medical Image Segmentation

Industrial Conference on Data Mining, 2020
Deep learning has proved to be a powerful tool for medical image analysis in recent years. Data imbalance is a common problem in medical images. Dice Loss is widely used in medical image segmentation tasks to address the data imbalance problem.
Rongjian Zhao, B. Qian, Xianli Zhang, Yang Li, Rong Wei, Yang Liu, Yinggang Pan   +6 more
semanticscholar   +1 more source

Grasping the dice by dicing the grasp

Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), 2004
Many methods for generating and analyzing grasps have been developed in the recent years. They gave insight and comprehension of grasping with robot hands but many of them are rather complicated to implement and of high computational complexity.
Borst, C., Fischer, M., Hirzinger, G.
openaire   +2 more sources

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019
The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces
J. Bertels, Tom Eelbode, Maxim Berman, D. Vandermeulen, F. Maes, R. Bisschops, Matthew B. Blaschko   +6 more
semanticscholar   +1 more source

Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning

Neural Information Processing Systems
One important property of DIstribution Correction Estimation (DICE) methods is that the solution is the optimal stationary distribution ratio between the optimized and data collection policy. In this work, we show that DICE-based methods can be viewed as
Liyuan Mao, Haoran Xu, Weinan Zhang, Xianyuan Zhan, Amy Zhang   +4 more
semanticscholar   +1 more source

Centerline Boundary Dice Loss for Vascular Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention
Vascular segmentation in medical imaging plays a crucial role in analysing morphological and functional assessments. Traditional methods, like the centerline Dice (clDice) loss, ensure topology preservation but falter in capturing geometric details ...
Pengcheng Shi, Jiesi Hu, Yanwu Yang, Zilve Gao, Wei Liu, Tingxia Ma   +5 more
semanticscholar   +1 more source

Image Segmentation Metrics in Skin Lesion: Accuracy, Sensitivity, Specificity, Dice Coefficient, Jaccard Index, and Matthews Correlation Coefficient

CENiM, 2020
One of the main problems in skin lesion detection is image segmentation. This method is essential not only for image processing-based but also for machine learning-based skin lesion detection to improve the performance.
A. W. Setiawan
semanticscholar   +1 more source