Ling Huang | DeepAI

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∙ 09/12/2023

Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation

Single-modality medical images generally do not contain enough informati...

0 Ling Huang, et al. ∙

∙ 09/12/2023

Medical Image Segmentation with Belief Function Theory and Deep Learning

Deep learning has shown promising contributions in medical image segment...

0 Ling Huang, et al. ∙

∙ 06/23/2022

Evidence fusion with contextual discounting for multi-modality medical image segmentation

As information sources are usually imperfect, it is necessary to take in...

0 Ling Huang, et al. ∙

∙ 05/03/2022

Application of belief functions to medical image segmentation: A review

Belief function theory, a formal framework for uncertainty analysis and ...

0 Ling Huang, et al. ∙

∙ 04/20/2022

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

Recently, Deep Neural Networks (DNNs) have been widely introduced into C...

0 Ling Huang, et al. ∙

∙ 03/12/2022

G^3SR: Global Graph Guided Session-based Recommendation

Session-based recommendation tries to make use of anonymous session data...

3 Zhi-Hong Deng, et al. ∙

∙ 01/31/2022

Lymphoma segmentation from 3D PET-CT images using a deep evidential network

An automatic evidential segmentation method based on Dempster-Shafer the...

0 Ling Huang, et al. ∙

∙ 08/11/2021

Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation

Lymphoma detection and segmentation from whole-body Positron Emission To...

0 Ling Huang, et al. ∙

∙ 04/27/2021

Evidential segmentation of 3D PET/CT images

PET and CT are two modalities widely used in medical image analysis. Acc...

0 Ling Huang, et al. ∙

∙ 03/10/2021

BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

Collaborative Filtering (CF) based recommendation methods have been wide...

10 Zi-Yuan Hu, et al. ∙

∙ 01/29/2021

Belief function-based semi-supervised learning for brain tumor segmentation

Precise segmentation of a lesion area is important for optimizing its tr...

0 Ling Huang, et al. ∙

∙ 01/18/2021

Covid-19 classification with deep neural network and belief functions

Computed tomography (CT) image provides useful information for radiologi...

0 Ling Huang, et al. ∙

∙ 12/15/2020

Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework

Since the label collecting is prohibitive and time-consuming, unsupervis...

0 Han Zhang, et al. ∙

∙ 01/15/2019

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

In general, recommendation can be viewed as a matching problem, i.e., ma...

8 Zhi-Hong Deng, et al. ∙

∙ 11/06/2018

DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction

Modern neural network models have achieved the state-of-the-art performa...

0 Shun Zheng, et al. ∙

∙ 10/16/2018

On Finding Dense Subgraphs in Bipartite Graphs: Linear Algorithms with Applications to Fraud Detection

Detecting dense subgraphs from large graphs is a core component in many ...

0 Yikun Ban, et al. ∙

∙ 10/15/2018

Bounding Entities within Dense Subtensors

Group-based fraud detection is a promising methodology to catch frauds o...

0 Yikun Ban, et al. ∙

∙ 05/25/2018

BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group Detection

Frauds severely hurt many kinds of Internet businesses. Group-based frau...

0 Yikun Ban, et al. ∙

∙ 03/15/2012

Online Semi-Supervised Learning on Quantized Graphs

In this paper, we tackle the problem of online semi-supervised learning ...

0 Michal Valko, et al. ∙

∙ 01/28/2011

An Analysis of the Convergence of Graph Laplacians

Existing approaches to analyzing the asymptotics of graph Laplacians typ...

0 Daniel Ting, et al. ∙

∙ 09/30/2010

Mantis: Predicting System Performance through Program Analysis and Modeling

We present Mantis, a new framework that automatically predicts program p...

0 Byung-Gon Chun, et al. ∙

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