Lecture notes-monograph series最新文献

Semi-supervised Machine Learning with MixMatch and Equivalence Classes 半监督机器学习与MixMatch和等价类

Lecture notes-monograph series Pub Date : 2020-10-04 DOI: 10.1007/978-3-030-61166-8_12

Colin B. Hansen, V. Nath, Riqiang Gao, Camilo Bermúdez, Yuankai Huo, K. Sandler, P. Massion, J. Blume, T. Lasko, B. Landman

引用次数: 1

Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers. 基于动量优化器的多点训练联邦梯度平均。

Lecture notes-monograph series Pub Date : 2020-10-01 Epub Date: 2020-09-26 DOI: 10.1007/978-3-030-60548-3_17

Samuel W Remedios, John A Butman, Bennett A Landman, Dzung L Pham

{"title":"Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers.","authors":"Samuel W Remedios, John A Butman, Bennett A Landman, Dzung L Pham","doi":"10.1007/978-3-030-60548-3_17","DOIUrl":"https://doi.org/10.1007/978-3-030-60548-3_17","url":null,"abstract":"Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning without data transfer that is mathematically equivalent to single site training with centralized data. We evaluate two scenarios: a simulated multi-site dataset for handwritten digit classification with MNIST and a real multi-site dataset with head CT hemorrhage segmentation. We compare federated gradient averaging to single site training, federated weight averaging (FWA), and cyclic weight transfer. In the MNIST task, we show that training with FGA results in a weight set equivalent to centralized single site training. In the hemorrhage segmentation task, we show that FGA achieves on average superior results to both FWA and cyclic weight transfer due to its ability to leverage momentum-based optimization.","PeriodicalId":93329,"journal":{"name":"Lecture notes-monograph series","volume":"12444 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442829/pdf/nihms-1687698.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39423625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Semi-supervised Machine Learning with MixMatch and Equivalence Classes. 半监督机器学习与MixMatch和等价类。

Lecture notes-monograph series Pub Date : 2020-01-01 Epub Date: 2020-10-02

Colin B Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L Sandler, Pierre P Massion, Jeffrey D Blume, Thomas A Lasko, Bennett A Landman

{"title":"Semi-supervised Machine Learning with MixMatch and Equivalence Classes.","authors":"Colin B Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L Sandler, Pierre P Massion, Jeffrey D Blume, Thomas A Lasko, Bennett A Landman","doi":"","DOIUrl":"","url":null,"abstract":"Semi-supervised methods have an increasing impact on computer vision tasks to make use of scarce labels on large datasets, yet these approaches have not been well translated to medical imaging. Of particular interest, the MixMatch method achieves significant performance improvement over popular semi-supervised learning methods with scarce labels in the CIFAR-10 dataset. In a complementary approach, Nullspace Tuning on equivalence classes offers the potential to leverage multiple subject scans when the ground truth for the subject is unknown. This work is the first to (1) explore MixMatch with Nullspace Tuning in the context of medical imaging and (2) characterize the impacts of the methods with diminishing labels. We consider two distinct medical imaging domains: skin lesion diagnosis and lung cancer prediction. In both cases we evaluate models trained with diminishing labeled data using supervised, MixMatch, and Nullspace Tuning methods as well as MixMatch with Nullspace Tuning together. MixMatch with Nullspace Tuning together is able to achieve an AUC of 0.755 in lung cancer diagnosis with only 200 labeled subjects on the National Lung Screening Trial and a balanced multi-class accuracy of 77% with only 779 labeled examples on HAM10000. This performance is similar to that of the fully supervised methods when all labels are available. In advancing data driven methods in medical imaging, it is important to consider the use of current state-of-the-art semi-supervised learning methods from the greater machine learning community and their impact on the limitations of data acquisition and annotation.","PeriodicalId":93329,"journal":{"name":"Lecture notes-monograph series","volume":"12446 ","pages":"112-121"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388309/pdf/nihms-1647070.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39366463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection 远距离LSTM:肺癌检测长短期记忆模型中的时间间隔门

Lecture notes-monograph series Pub Date : 2019-09-11 DOI: 10.1007/978-3-030-32692-0_36

Riqiang Gao, Yuankai Huo, S. Bao, Yucheng Tang, S. Antic, Emily S. Epstein, A. Balar, S. Deppen, Alexis B. Paulson, K. Sandler, P. Massion, B. Landman

引用次数: 36

A Modified Kendall Rank-Order Association Test For Evaluating The Repeatability Of Two Studies With A Large Number Of Objects. 评价两项具有大量对象的研究的可重复性的改进肯德尔秩序关联检验。

Lecture notes-monograph series Pub Date : 2007-03-01 DOI: 10.1142/9789812708298_0025

T. Zheng, S. Lo

引用次数: 0