Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22875
Abstract: Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods.… read more here.
Keywords: visual representation; representation learning; federated learning; learning ... See more keywords
Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method.
Sign Up to like & getrecommendations! Published in 2024 at "Medical physics"
DOI: 10.1002/mp.17401
Abstract: BACKGROUND Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival. PURPOSE To investigate the performance of a representation learning-based feature fusion strategy that employs a multiphase contrast-enhanced CT… read more here.
Keywords: mtm hcc; macrotrabecular massive; massive hepatocellular; representation learning ... See more keywords
Extracting metadata of scientific references in patents based on combination of representation learning and machine learning
Sign Up to like & getrecommendations! Published in 2018 at "Proceedings of the Association for Information Science and Technology"
DOI: 10.1002/pra2.2018.14505501189
Abstract: The scientific references in patent (SRPs), one type of non‐patent references (NPRs), is the best representative of scientific knowledge cited in patents (SKCP). However, SKCP is often indicated by NPRs rather than SRPs currently, which… read more here.
Keywords: representation learning; machine learning; metadata; scientific references ... See more keywords
A survey: knowledge graph entity alignment research based on graph embedding
Sign Up to like & getrecommendations! Published in 2024 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-024-10866-4
Abstract: Entity alignment (EA) aims to automatically match entities in different knowledge graphs, which is beneficial to the development of knowledge-driven applications. Representation learning has powerful feature capture capability and it is widely used in the… read more here.
Keywords: knowledge; entity alignment; representation learning; research ... See more keywords
Unsupervised Binary Representation Learning with Deep Variational Networks
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01166-4
Abstract: Learning to hash is regarded as an efficient approach for image retrieval and many other big-data applications. Recently, deep learning frameworks are adopted for image hashing, suggesting an alternative way to formulate the encoding function… read more here.
Keywords: representation learning; learning deep; deep variational; unsupervised binary ... See more keywords
Physical Representation Learning and Parameter Identification from Video Using Differentiable Physics
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-021-01493-5
Abstract: Representation learning for video is increasingly gaining attention in the field of computer vision. For instance, video prediction models enable activity and scene forecasting or vision-based planning and control. In this article, we investigate the… read more here.
Keywords: differentiable physics; representation learning; physics; physical representation ... See more keywords
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision
Sign Up to like & getrecommendations! Published in 2025 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-025-02391-w
Abstract: Omnidirectional image (ODI) data is captured with a field-of-view of 360∘×180∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$360^\circ \times 180^\circ $$\end{document}, which is much wider than the pinhole cameras and captures richer surrounding… read more here.
Keywords: document; representation learning; vision; optimization strategies ... See more keywords
Domain Adaptation for Graph Representation Learning: Challenges, Progress, and Prospects
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Computer Science and Technology"
DOI: 10.1007/s11390-024-4465-x
Abstract: Graph representation learning often faces knowledge scarcity in real-world applications, including limited labels and sparse relationships. Although a range of methods have been proposed to address these problems, such as graph few-shot learning, they mainly… read more here.
Keywords: graph representation; graph; domain adaptation; representation learning ... See more keywords
MGPOOL: multi-granular graph pooling convolutional networks representation learning
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-021-01328-2
Abstract: Graph convolutional network (GCN) nowadays become new state-of-the-art for networks representation learning. Most of the existing methods are single-granular methods that failed to analyze the graph at multi-granular views so as to lose abundant information.… read more here.
Keywords: coarsest graph; multi granular; networks representation; graph ... See more keywords
SURREAL: Subgraph Robust Representation Learning
Sign Up to like & getrecommendations! Published in 2019 at "Applied Network Science"
DOI: 10.1007/s41109-019-0160-1
Abstract: The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim… read more here.
Keywords: representation learning; surreal subgraph; graph; subgraph robust ... See more keywords
Representation learning applications in biological sequence analysis
Sign Up to like & getrecommendations! Published in 2021 at "Computational and Structural Biotechnology Journal"
DOI: 10.1016/j.csbj.2021.05.039
Abstract: Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of… read more here.
Keywords: sequence analysis; representation learning; biological sequence;