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Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [article]

Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng
2021 arXiv   pre-print
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]

Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou
2022 arXiv   pre-print
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]

Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang
2021 arXiv   pre-print
, 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]

Yi Liu, Xingliang Yuan, Ruihui Zhao, Cong Wang, Dusit Niyato, Yefeng Zheng
2022 arXiv   pre-print
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]

Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Marco Ciccone, Emanuele Rodolà, Barbara Caputo
2021 arXiv   pre-print
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]

Bach Ngoc Kim, Jose Dolz, Pierre-Marc Jodoin, Christian Desrosiers
2020 arXiv   pre-print
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

David J Foran, Lin Yang, Wenjin Chen, Jun Hu, Lauri A Goodell, Michael Reiss, Fusheng Wang, Tahsin Kurc, Tony Pan, Ashish Sharma, Joel H Saltz
2011 JAMIA Journal of the American Medical Informatics Association  
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]

Liang Peng, Nan Wang, Nicha Dvornek, Xiaofeng Zhu, Xiaoxiao Li
2022 arXiv   pre-print
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]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
[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

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
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

Divya Jatain, Vikram Singh, Naveen Dahiya
2021 Journal of King Saud University: Computer and Information Sciences  
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

Denis Ullmann, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Brandon Panos, Slava Voloshynovskiy
2020 Entropy  
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]

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
2022 arXiv   pre-print
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

Yixue Hao, Yiming Miao, Min Chen, Hamid Gharavi, Victor C. M. Leung
2021 Big Data and Cognitive Computing  
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

Xieling Chen, Gary Cheng, Fu Lee Wang, Xiaohui Tao, Haoran Xie, Lingling Xu
2022 Brain Informatics  
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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