Proceedings of machine learning research最新文献_第9页

Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context. 自动颅内血管标记与学习促进血管连接，半径和空间背景。

Proceedings of machine learning research Pub Date : 2022-11-01

Jannik Sobisch, Žiga Bizjak, Aichi Chien, Žiga Špiclin

{"title":"Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context.","authors":"Jannik Sobisch, Žiga Bizjak, Aichi Chien, Žiga Špiclin","doi":"","DOIUrl":"","url":null,"abstract":"Cerebrovascular diseases are among the world's top causes of death and their screening and diagnosis rely on angiographic imaging. We focused on automated anatomical labeling of cerebral arteries that enables their cross-sectional quantification and inter-subject comparisons and thereby identification of geometric risk factors correlated to the cerebrovascular diseases. We used 152 cerebral TOF-MRA angiograms from three publicly available datasets and manually created reference labeling using Slicer3D. We extracted centerlines from nnU-net based segmentations using VesselVio and labeled them according to the reference labeling. Vessel centerline coordinates, in combination with additional vessel connectivity, radius and spatial context features were used for training seven distinct PointNet++ models. Model trained solely on the vessel centerline coordinates resulted in ACC of 0.93 and across-labels average TPR was 0.88. Including vessel radius significantly improved ACC to 0.95, and average TPR to 0.91. Finally, focusing spatial context to the Circle of Willis are resulted in best ACC of 0.96 and best average TPR of 0.93. Hence, using vessel radius and spatial context greatly improved vessel labeling, with the attained perfomance opening the avenue for clinical applications of intracranial vessel labeling.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"194 ","pages":"34-44"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112880/pdf/nihms-1889674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9389427","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

An Extensive Data Processing Pipeline for MIMIC-IV. 用于MIMIC-IV的扩展数据处理管道。

Proceedings of machine learning research Pub Date : 2022-11-01

Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti

{"title":"An Extensive Data Processing Pipeline for MIMIC-IV.","authors":"Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of research has exposed the challenges of the accessibility of EHRs. MIMIC is a popular, public, and free EHR dataset in a raw format that has been used in numerous studies. The absence of standardized preprocessing steps can be, however, a significant barrier to the wider adoption of this rare resource. Additionally, this absence can reduce the reproducibility of the developed tools and limit the ability to compare the results among similar studies. In this work, we provide a greatly customizable pipeline to extract, clean, and preprocess the data available in the fourth version of the MIMIC dataset (MIMIC-IV). The pipeline also presents an end-to-end wizard-like package supporting predictive model creations and evaluations. The pipeline covers a range of clinical prediction tasks which can be broadly classified into four categories - readmission, length of stay, mortality, and phenotype prediction. The tool is publicly available at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 ","pages":"311-325"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854277/pdf/nihms-1865425.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10604378","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

Contrastive Representation Learning for Gaze Estimation 注视估计的对比表征学习

Proceedings of machine learning research Pub Date : 2022-10-24 DOI: 10.48550/arXiv.2210.13404

Swati Jindal, R. Manduchi

引用次数: 4

A Path Towards Clinical Adaptation of Accelerated MRI 加速MRI的临床适应之路

Proceedings of machine learning research Pub Date : 2022-08-26 DOI: 10.48550/arXiv.2208.12835

Michael S. Yao, M. Hansen

引用次数: 0

DIET: Conditional independence testing with marginal dependence measures of residual information DIET：剩余信息的边际依赖性度量的条件独立性检验

Proceedings of machine learning research Pub Date : 2022-08-18 DOI: 10.48550/arXiv.2208.08579

Mukund Sudarshan, A. Puli, Wesley Tansey, R. Ranganath

{"title":"DIET: Conditional independence testing with marginal dependence measures of residual information","authors":"Mukund Sudarshan, A. Puli, Wesley Tansey, R. Ranganath","doi":"10.48550/arXiv.2208.08579","DOIUrl":"https://doi.org/10.48550/arXiv.2208.08579","url":null,"abstract":"Conditional randomization tests (CRTs) assess whether a variable x is predictive of another variable y, having observed covariates z. CRTs require fitting a large number of predictive models, which is often computationally intractable. Existing solutions to reduce the cost of CRTs typically split the dataset into a train and test portion, or rely on heuristics for interactions, both of which lead to a loss in power. We propose the decoupled independence test (DIET), an algorithm that avoids both of these issues by leveraging marginal independence statistics to test conditional independence relationships. DIET tests the marginal independence of two random variables: Fx∣z(x∣z) and Fy∣z(y∣z) where F⋅∣z(⋅∣z) is a conditional cumulative distribution function (CDF) for the distribution p(⋅∣z). These variables are termed \"information residuals.\" We give sufficient conditions for DIET to achieve finite sample type-1 error control and power greater than the type-1 error rate. We then prove that when using the mutual information between the information residuals as a test statistic, DIET yields the most powerful conditionally valid test. Finally, we show DIET achieves higher power than other tractable CRTs on several synthetic and real benchmarks.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 1","pages":"10343-10367"},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43051329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness 临床存在下的推断策略：对算法公平性的影响

Proceedings of machine learning research Pub Date : 2022-08-13 DOI: 10.48550/arXiv.2208.06648

V. Jeanselme, Maria De-Arteaga, Zhe Zhang, J. Barrett, Brian D. M. Tom

{"title":"Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness","authors":"V. Jeanselme, Maria De-Arteaga, Zhe Zhang, J. Barrett, Brian D. M. Tom","doi":"10.48550/arXiv.2208.06648","DOIUrl":"https://doi.org/10.48550/arXiv.2208.06648","url":null,"abstract":"Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and groupspecific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from a neglected step of the machine learning pipeline.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 1","pages":"12 - 34"},"PeriodicalIF":0.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43981392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Bias Aware Probabilistic Boolean Matrix Factorization. 偏差感知概率布尔矩阵分解。

Proceedings of machine learning research Pub Date : 2022-08-01

Changlin Wan, Pengtao Dang, Tong Zhao, Yong Zang, Chi Zhang, Sha Cao

{"title":"Bias Aware Probabilistic Boolean Matrix Factorization.","authors":"Changlin Wan, Pengtao Dang, Tong Zhao, Yong Zang, Chi Zhang, Sha Cao","doi":"","DOIUrl":"","url":null,"abstract":"Boolean matrix factorization (BMF) is a combinatorial problem arising from a wide range of applications including recommendation system, collaborative filtering, and dimensionality reduction. Currently, the noise model of existing BMF methods is often assumed to be homoscedastic; however, in real world data scenarios, the deviations of observed data from their true values are almost surely diverse due to stochastic noises, making each data point not equally suitable for fitting a model. In this case, it is not ideal to treat all data points as equally distributed. Motivated by such observations, we introduce a probabilistic BMF model that recognizes the object- and feature-wise bias distribution respectively, called bias aware BMF (BABF). To the best of our knowledge, BABF is the first approach for Boolean decomposition with consideration of the feature-wise and object-wise bias in binary data. We conducted experiments on datasets with different levels of background noise, bias level, and sizes of the signal patterns, to test the effectiveness of our method in various scenarios. We demonstrated that our model outperforms the state-of-the-art factorization methods in both accuracy and efficiency in recovering the original datasets, and the inferred bias level is highly significantly correlated with true existing bias in both simulated and real world datasets.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"180 ","pages":"2035-2044"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10421704/pdf/nihms-1891928.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10060510","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

Survival Mixture Density Networks 生存混合密度网络

Proceedings of machine learning research Pub Date : 2022-08-01 DOI: 10.48550/arXiv.2208.10759

Xintian Han, Mark Goldstein, R. Ranganath

引用次数: 3

Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records. 用半监督变压器进行电子健康记录的短时间学习。

Proceedings of machine learning research Pub Date : 2022-08-01

Raphael Poulain, Mehak Gupta, Rahmatollah Beheshti

{"title":"Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records.","authors":"Raphael Poulain, Mehak Gupta, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"With the growing availability of Electronic Health Records (EHRs), many deep learning methods have been developed to leverage such datasets in medical prediction tasks. Notably, transformer-based architectures have proven to be highly effective for EHRs. Transformer-based architectures are generally very effective in \"transferring\" the acquired knowledge from very large datasets to smaller target datasets through their comprehensive \"pre-training\" process. However, to work efficiently, they still rely on the target datasets for the downstream tasks, and if the target dataset is (very) small, the performance of downstream models can degrade rapidly. In biomedical applications, it is common to only have access to small datasets, for instance, when studying rare diseases, invasive procedures, or using restrictive cohort selection processes. In this study, we present CEHR-GAN-BERT, a semi-supervised transformer-based architecture that leverages both in- and out-of-cohort patients to learn better patient representations in the context of few-shot learning. The proposed method opens new learning opportunities where only a few hundred samples are available. We extensively evaluate our method on four prediction tasks and three public datasets showing the ability of our model to achieve improvements upwards of 5% on all performance metrics (including AUROC and F1 Score) on the tasks that use less than 200 annotated patients during the training process.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"182 ","pages":"853-873"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399128/pdf/nihms-1865430.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9946062","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

Online Weak-form Sparse Identification of Partial Differential Equations. 偏微分方程的在线弱式稀疏识别。

Proceedings of machine learning research Pub Date : 2022-08-01

Daniel A Messenger, Emiliano Dall'anese, David M Bortz

{"title":"Online Weak-form Sparse Identification of Partial Differential Equations.","authors":"Daniel A Messenger, Emiliano Dall'anese, David M Bortz","doi":"","DOIUrl":"","url":null,"abstract":"This paper presents an online algorithm for identification of partial differential equations (PDEs) based on the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy). The algorithm is online in the sense that if performs the identification task by processing solution snapshots that arrive sequentially. The core of the method combines a weak-form discretization of candidate PDEs with an online proximal gradient descent approach to the sparse regression problem. In particular, we do not regularize the <math><mrow><msub><mi>ℓ</mi><mn>0</mn></msub></mrow></math>-pseudo-norm, instead finding that directly applying its proximal operator (which corresponds to a hard thresholding) leads to efficient online system identification from noisy data. We demonstrate the success of the method on the Kuramoto-Sivashinsky equation, the nonlinear wave equation with time-varying wavespeed, and the linear wave equation, in one, two, and three spatial dimensions, respectively. In particular, our examples show that the method is capable of identifying and tracking systems with coefficients that vary abruptly in time, and offers a streaming alternative to problems in higher dimensions.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"190 ","pages":"241-256"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10805452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139543114","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