Search Results for author:
Found 21 papers, 10 papers with code
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
Medical Image Segmentation with Belief Function Theory and Deep Learning
Second, we present a semi-supervised medical image segmentation framework to decrease the uncertainty caused by the lack of annotations with evidential segmentation and evidence fusion.
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis.
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
Evidence fusion with contextual discounting for multi-modality medical image segmentation
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks.
Application of belief functions to medical image segmentation: A review
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation.
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users. However, the DNNs-based models usually suffer from high computational complexity, i. e., consuming very long training time and storing huge amount of trainable parameters.
G$^3$SR: Global Graph Guided Session-based Recommendation
G$^3$SR decomposes the session-based recommendation workflow into two steps.
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
The architecture is composed of a deep feature-extraction module and an evidential layer.
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of evidence using Dempster-Shafer theory (DST).
Evidential segmentation of 3D PET/CT images
In this paper, a segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images.
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Representation learning tries to learn a common low dimensional space for the representations of users and items.
Belief function-based semi-supervised learning for brain tumor segmentation
Precise segmentation of a lesion area is important for optimizing its treatment.
Covid-19 classification with deep neural network and belief functions
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19.
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Meanwhile, such applications usually require modeling the intrinsic clusters in high-dimensional data, which usually displays heterogeneous statistical patterns as the patterns of different clusters may appear in different dimensions.
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.
Ranked #1 on
Community Detection
on Cora
Social and Information Networks Physics and Society 97R40
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
To solve this problem, many methods have been studied, which can be generally categorized into two types, i. e., representation learning-based CF methods and matching function learning-based CF methods.
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
Large-Margin Convex Polytope Machine
We present the Convex Polytope Machine (CPM), a novel non-linear learning algorithm for large-scale binary classification tasks.
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
Our two SPORE algorithms are able to build relationships between responses (e. g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable.
Spectral Clustering with Perturbed Data
We show that the error under perturbation of spectral clustering is closely related to the perturbation of the eigenvectors of the Laplacian matrix.
