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Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
[article]
2021
arXiv
pre-print
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We validate our method on two large-scale medical image classification datasets. ...
We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. ...
over Fed-Consistency which does not employ our inter-client relation matching scheme. ...
arXiv:2106.08600v1
fatcat:th2ifmevovfgzdz42e3dvi4jui
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
[article]
2022
arXiv
pre-print
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Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use ...
In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount ...
into FL, the FedIRM (MICCAI'21) [14] which enhances the consistency regularization with an inter-client relation matching, and the Fed-Match (ICLR'21) [10] which applies inter-client consistency and ...
arXiv:2206.13079v1
fatcat:nrjhnijtufea5c5hx6mousdami
Federated Semi-Supervised Learning with Inter-Client Consistency Disjoint Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.12097v2 fatcat:oa7lsglmoved5i3o6ie22lh37aOther Versions
, namely Federated Semi-Supervised Learning (FSSL). ...
FedMatch improves upon naive combinations of federated learning and semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters for disjoint learning ...
Federated Semi-Supervised Learning We introduce a realistic federated learning scenario, Federated Semi-Supervised Learning (FSSL). ...
arXiv:2006.12097v3
fatcat:znubc5dbsbcqhaift6rjeeftuu
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses
[article]
2022
arXiv
pre-print
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Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. ...
In practice, these SSFL systems implement semi-supervised training by assigning a "guessed" label to the unlabeled data near the labeled data to convert the unsupervised problem into a fully supervised ...
Overview of the semi-supervised federated learning system.
Fig. 2 . 2 Fig. 2. Our proposed poisoning attacks in SSFL.
et al. proposed inter-client consistency loss to train SSFL. ...
arXiv:2012.04432v2
fatcat:3wxbf2twhfcopenn2u3shyffoi
Cluster-driven Graph Federated Learning over Multiple Domains
[article]
2021
arXiv
pre-print
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Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). ...
Here we propose a novel Cluster-driven Graph Federated Learning (FedCG). ...
GraphFL, instead, is a semi-supervised node classification method on graphs and uses the FL scenario to solve real-world graph-based problems. ...
arXiv:2104.14628v1
fatcat:mqzqznns5bfy7hrloyihjjiqhe
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images
[article]
2020
arXiv
pre-print
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This paper presents a client/server privacy-preserving network in the context of multicentric medical image analysis. ...
subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case). ...
This principle is at the core of powerful regularization techniques for semi-supervised learning, such as Virtual Adversarial Training (VAT) [57] . 5) Dimension of encoded images: By default, our encoder ...
arXiv:1909.04087v3
fatcat:77rjoq3jgjhdzhoz2kahgu75q4
ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology
2011
JAMIA Journal of the American Medical Informatics Association
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It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support ...
can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified ...
The data analysis and data management components of ImageMiner can be accessed from remote clients via service interfaces, and multiple ImageMiner deployments can be federated in a distributed setting. ...
doi:10.1136/amiajnl-2011-000170
pmid:21606133
pmcid:PMC3128405
fatcat:v4bxdoqb55fpzc4sm6hqbbigp4
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
[article]
2022
arXiv
pre-print
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In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. ...
However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. ...
For example, Parisot et al. applies GCN for semi-supervised disease prediction on neuroimaging data, where nodes are defined as subjects and an edge represents the interaction and association between two ...
arXiv:2112.10166v2
fatcat:okilyrurq5bphjbl4k3spprhmu
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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[279] proposed a FL framework to perform graphbased semi-supervised node classification to address these challenges. ...
Their semi-supervised method showed better performance in comparison to a standard linear classifier (which only considered the individual features for classification). ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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Resolution for Transfer Learning-
Based Skin Lesion Classification
DAY 3 -Jan 14, 2021
Lin, Qiufan; Fouchez, Dominique;
Pasquet, Jérôme
1538
Galaxy Image Translation with Semi-Supervised Noise ...
DAY 4 -Jan 15, 2021 Wang, ShuWei; Wang, Qiuyun; Jiang, Zhengwei; Wang, Xuren; Jing, RongQi 1077 A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
A Contemplative Perspective on Federated Machine Learning: Taxonomy, Threats & Vulnerability Assessment and Challenges
2021
Journal of King Saud University: Computer and Information Sciences
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Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. ...
This paper intends to address the totality of federated learning with a complete vulnerability assessment. ...
, videos, images etc. ...
doi:10.1016/j.jksuci.2021.05.016
fatcat:6gynsax3xreyfit5vlyyno3jiy
Information Bottleneck Classification in Extremely Distributed Systems
2020
Entropy
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We present a new decentralized classification system based on a distributed architecture. ...
The final classification is performed at the centralized classifier that votes for the class with the minimum reconstruction distortion. ...
Studies such as [16, 17] showed that semi-supervised classification is even a more challenging task for such systems. ...
doi:10.3390/e22111237
pmid:33287005
pmcid:PMC7711965
fatcat:lxua4vulvbcbfel2ihuaoa2yiu
Learning Disentangled Representations in the Imaging Domain
[article]
2022
arXiv
pre-print
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We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications. ...
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. ...
Tsaftaris acknowledges the support of Canon Medical and the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme (grant RCSRF1819\8\25). ...
arXiv:2108.12043v5
fatcat:cbpmp6pbajhjvjzovulswuj2wy
6G Cognitive Information Theory: A Mailbox Perspective
2021
Big Data and Cognitive Computing
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In wise medical, data-fusion technology is used for medical image registration and retrieval [72] , multi-source image-feature fusion, multi-sensor fusion of medical apparatus and instruments or body ...
In this system, entities can be searched via the knowledge graph based on a user's preference and relations of things in order to make things match the users' preference. ...
doi:10.3390/bdcc5040056
fatcat:ffof5likzbhfnopa3yfaobznfa
Machine and cognitive intelligence for human health: systematic review
2022
Brain Informatics
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Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. ...
topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification ...
In terms of monitoring via medical imaging, Hu et al. ...
doi:10.1186/s40708-022-00153-9
pmid:35150379
pmcid:PMC8840949
fatcat:whia7d7zyze5rd6susl54ozcqq
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