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Federated Learning for Breast Density Classification: A Real-World Implementation [article]

Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch (+31 others)
2020 arXiv   pre-print
In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting.  ...  Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS).  ...  Acknowledgements: Research reported in this publication was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health  ... 
arXiv:2009.01871v2 fatcat:sjxfkix7mbbpvkwsfcos2dosty

Feature selection and classification using flexible neural tree

Yuehui Chen, Ajith Abraham, Bo Yang
2006 Neurocomputing  
The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification.  ...  The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features.  ...  Authors would like to thank the anonymous referees for the technical suggestions and remarks which helped to improve the contents and the quality of presentation.  ... 
doi:10.1016/j.neucom.2006.01.022 fatcat:pp3pqkctfvgrrngq2w5o6aqlee

Robust classification with reject option using the self-organizing map

Ricardo Gamelas Sousa, Ajalmar R. Rocha Neto, Jaime S. Cardoso, Guilherme A. Barreto
2015 Neural computing & applications (Print)  
For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world data sets.  ...  The rejected item is then handled by a different classifier or by a human expert.  ...  This work was also partially funded by Fundação para a Ciência e a Tecnologia (FCT) -Portugal through project PTDC/SAU-ENB/114951/2009 and by FEDER funds through the Programa Operacional Factores de Competitividade  ... 
doi:10.1007/s00521-015-1822-2 fatcat:of275fcgbvax3oirwmyiznwdk4

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data [article]

Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, Bert Arnrich
2022 arXiv   pre-print
This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against privacy attacks on DenseNet121 and ResNet50 network architectures.  ...  Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context.  ...  [24] deploy a DenseNet121 model for a real-world, physically distributed implementation of federated learning on CheXpert. Banerjee et al.  ... 
arXiv:2205.03168v1 fatcat:qcvtl2pfcjglhed6pvhw7unf6e

Boosting fuzzy rules in classification problems under single-winner inference

Luciano Sánchez, José Otero
2007 International Journal of Intelligent Systems  
In previous studies, we have shown that an Adaboost-based fitness can be successfully combined with a Genetic Algorithm to iteratively learn fuzzy rules from examples in classification problems.  ...  In this work we introduce our first results in the search of a boosting-based genetic method able to learn weighted fuzzy rules that are compatible with this last inference method.  ...  Acknowledgments This work was funded by Spanish M. of Science and Technology and by FEDER funds, under the grant TIC-04036-C05-05.  ... 
doi:10.1002/int.20236 fatcat:n4zuhskhxjbvfjhadez37yl5cu

A multi-reconstruction study of breast density estimation using Deep Learning [article]

Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal
2022 arXiv   pre-print
Deep-learning studies for breast density estimation use only a single modality for training a neural network. However, doing so restricts the number of images in the dataset.  ...  There have been efforts in the direction of automating a breast density classification pipeline. Breast density estimation is one of the key tasks performed during a screening exam.  ...  learning for breast density classification.  ... 
arXiv:2202.08238v2 fatcat:lif4zbg65zdtho75ia3kcqe7ie

Exploitation of Pairwise Class Distances for Ordinal Classification

J. Sánchez-Monedero, Pedro A. Gutiérrez, Peter Tiňo, C. Hervás-Martínez
2013 Neural Computation  
The proposed approach is extensively evaluated with eight nominal and ordinal classifiers methods, ten real world ordinal classification datasets, and four different performance measures.  ...  Some ordinal classification approaches perform a projection from the input space to 1-dimensional (latent) space that is partitioned into a sequence of intervals (one for each class).  ...  The work of Peter Tiňo was supported by a BBSRC grant (no. BB/H012508/1).  ... 
doi:10.1162/neco_a_00478 pmid:23663143 fatcat:fn7yprtn2feqhbiaiipnlsjnvq

Ensemble extraction for classification and detection of bird species

Eric P. Kasten, Philip K. McKinley, Stuart H. Gage
2010 Ecological Informatics  
Advances in technology have enabled new approaches for sensing the environment and collecting data about the world.  ...  Our goal is automated detection and classification of various species of birds.  ...  The authors would like to thank Ron Fox and Wooyeong Joo at Michigan State University for their contributions to this work.  ... 
doi:10.1016/j.ecoinf.2010.02.003 fatcat:j62fzd2wt5dtjhp3hlhcrkypb4

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
A first implementation was limited to the calculation of regions in 2-dimensions only, which prevents it from being applied to create designs for real world experiments.  ...  A real dataset is analyzed using the R package bild where this methodology is implemented.  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Francisco Fernández-Navarro, César Hervás-Martínez, Roberto Ruiz, Jose C. Riquelme
2012 Applied Soft Computing  
An interesting property of the GRBF is that it can continuously and smoothly repro-duce different RBFs by changing a real parameter .  ...  Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN  ...  -3745 project of the "Junta de AndalucA-a" (Spain).  ... 
doi:10.1016/j.asoc.2012.01.008 fatcat:r4llsci2wrhlhjptblkb6wl2gq

Crowdsourcing classification and causality to power a search-and-innovation engine [chapter]

Richard Absalom
2022 Knowledge Organization and Management in the Domain of Environment and Earth Observation (KOMEEO)  
A recent reappraisal of the project design recognised the potential of causality for modelling and matching problems. This paper proposes a design compatible with the crowdsourced classification.  ...  Durham Zoo (DZ) is a project to create a search-and-innovation engine for science and technology.  ...  The FITS file format for the long-term preservation of digital objects ...e Wells, Donald C., and Eric W. Greisen. 1979  ... 
doi:10.5771/9783956508752-13 fatcat:a243zpohn5aq7cgdlnui46p2sy

Comparative Descriptive Analysis of Breast Cancer Tissues Using K-means and SelfOrganizing Map

Alaba T. Owoseni, Olatubosun Olabode, Kolawole G. Akintola
2018 International Journal of Information Technology and Computer Science  
Result of the performances of these machine learning algorithms as implemented with R i368 version 3.4.2 shows a slight outperformance of K-means in terms of classification accuracy over self-organizing  ...  map for the considered dataset.  ...  Olabode of the Department of Computer Sciences, Federal University of Technology, Akure, Nigeria and all members of staff and students of Departments of Computer Science, Federal University of Technology  ... 
doi:10.5815/ijitcs.2018.08.07 fatcat:26f2p55qezce5lelgu2mm6gwe4

Availability and accessibility of perinatal data for the Robson classification of caesarean sections in Switzerland

2018 Swiss Medical Weekly  
However, all classifications include a category for congenital anomalies.  ...  Data linkage and the associated need for data protection is an area where countries have a lot to learn from each other and can benefit from sharing experiences.  ... 
doi:10.4414/smw.2018.14578 pmid:29376551 fatcat:3ayyn4upy5aofkvkzbjzjmxkie

Frequency of injuries and health status of football players in Bosnia; classification by gender and age

Ratko Peric, Radojka Peric
2013 Zenodo  
METHODS: Research was conducted at the Institute for Sport and Occupational Medicine in Banja Luka, Bosnia in time period of one year.  ...  Football has always been most represented and most popular sport among youngsters and the older population around the world. But is it really that healthy?  ...  Recently medicals agree on a consensus for their classification.  ... 
doi:10.5281/zenodo.4575221 fatcat:gps3ldi3crhmhjxoandbui77je

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Aimilia Gastounioti, Shyam Desai, Vinayak S. Ahluwalia, Emily F. Conant, Despina Kontos
2022 Breast Cancer Research  
Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's  ...  the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.  ...  A A visual display of the range of BI-RADS density classifications for AI models trained with different architectures and training parameters for 50 patients in the testing set.  ... 
doi:10.1186/s13058-022-01509-z pmid:35184757 pmcid:PMC8859891 fatcat:n5rs4ponqva63csdsgudqzmozy
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