检索结果-内蒙古大学图书馆

Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

学校读者我要写书评

暂无评论

Improving the Quality of Diabetic Data with Large Language M...

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sundaresan, Vaanathi Dinsdale, Nicola K. Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © 2024, CC BY.

关键词： Magnetic resonance imaging

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

学校读者我要写书评

暂无评论

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

On the Origins of Linear Representations in Large Language Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jiang, Yibo Rajendran, Goutham Ravikumar, Pradeep Aragam, Bryon Veitch, Victor Department of Computer Science University of Chicago United States Machine Learning Department Carnegie Mellon University United States Booth School of Business University of Chicago United States Department of Statistics University of Chicago United States Data Science Institute University of Chicago United States

Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

Federated learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems

学校读者我要写书评

暂无评论

Journal of the American College of Radiology 2022年第8期19卷 969-974页

作者： Darzidehkalani, Erfan Ghasemi-rad, Mohammad van Ooijen, P.M.A. Department of Radiotherapy University Medical Center Groningen University of Groningen Groningen Netherlands Machine Learning Lab Data Science Center in Health University Medical Center Groningen University of Groningen Netherlands Assistant Professor of Radiology Department of Interventional Radiology Baylor College of Medicine Houston Texas Department of Radiotherapy University Medical Center Groningen University of Groningen Groningen Netherlands Coordinator Machine Learning Lab Data Science Center in Health University Medical Center Groningen University of Groningen Netherlands

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging. © 2022 American College of Radiology

关键词： Federated learning medical imaging privacy-preserving machine learning

Protocon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-Supervised learning

学校读者我要写书评

暂无评论

Protocon: Pseudo-Label Refinement via Online Clustering and ...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Islam Nassar Munawar Hayat Ehsan Abbasnejad Hamid Rezatofighi Gholamreza Haffari Data Science and AI Department Monash University Australia Australian Institute for Machine Learning The University of Adelaide Australia

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose Protocon, a novel SSL method aimed at the less-explored label-scarce SSL where such methods usually underperform. Protocon refines the pseudolabels by lever-aging their nearest neighbours' information. The neighbours are identified as the training proceeds using an online clustering approach operating in an embedding space trained via a prototypical loss to encourage well-formed clusters. The online nature of Protocon allows it to utilise the label history of the entire dataset in one training cycle to refine labels in the following cycle without the need to store image embeddings. Hence, it can seamlessly scale to larger datasets at a low cost. Finally, Protocon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss. It delivers significant gains and faster convergence over state-of-the-art across 5 datasets, including CIFARs, ImageNet and DomainNet.

关键词：

Classification under nuisance parameters and generalized label shift in likelihood-free inference 24

学校读者我要写书评

暂无评论

Classification under nuisance parameters and generalized lab...

Proceedings of the 41st International Conference on machine learning

作者： Luca Masserano Alex Shen Michele Doro Tommaso Dorigo Rafael Izbicki Ann B. Lee Department of Statistics and Data Science and Machine Learning Department Carnegie Mellon University Pittsburgh Department of Statistics and Data Science Carnegie Mellon University Pittsburgh Department of Physics and Astronomy Università di Padova Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy and Lulea Techniska Universitet Lulea Sweden and Universal Scientific Education and Research Network Italy Department of Statistics Universidade Federal de São Carlos São Paulo Brazil

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data X as covariates leads to biased predictions and invalid uncertainty estimates of labels Y. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pretrained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models.

关键词：

DETECTING STATE CHANGES IN FUNCTIONAL NEURONAL CONNECTIVITY USING FACTORIAL SWITCHING LINEAR DYNAMICAL SYSTEMS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Gong, Yiwei Mierau, Susanna B. Williamson, Sinead A. Department of Statistics and Data sciences The University of Texas at Austin United States Department of Neurology Brigham and Women's Hospital Harvard Medical School United States Apple Machine Learning Research Italy

A key question in brain sciences is how to identify time-evolving functional connectivity, such as that obtained from recordings of neuronal activity over time. We wish to explain the observed phenomena in terms of latent states which, in the case of neuronal activity, might correspond to subnetworks of neurons within a brain or organoid. Many existing approaches assume that only one latent state can be active at a time, in contrast to our domain knowledge. We propose a switching dynamical system based on the factorial hidden Markov model. Unlike existing approaches, our model acknowledges that neuronal activity can be caused by multiple subnetworks, which may be activated either jointly or independently. A change in one part of the network does not mean that the entire connectivity pattern will change. We pair our model with scalable variational inference algorithm, using a concrete relaxation of the underlying factorial hidden Markov model, to effectively infer the latent states and model parameters. We show that our algorithm can recover ground-truth structure and yield insights about the maturation of neuronal activity in microelectrode array recordings from in vitro neuronal *** Codes 37N25, 62M05, 62P10, 62M10 Copyright © 2024, The Authors. All rights reserved.

关键词： Hidden Markov models

PROTOCON: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Nassar, Islam Hayat, Munawar Abbasnejad, Ehsan Rezatofighi, Hamid Haffari, Gholamreza Data Science and AI Department Monash University Australia Australian Institute for Machine Learning The University of Adelaide Australia

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose PROTOCON, a novel SSL method aimed at the less-explored label-scarce SSL where such methods usually underperform. PROTOCON refines the pseudo-labels by leveraging their nearest neighbours' information. The neighbours are identified as the training proceeds using an online clustering approach operating in an embedding space trained via a prototypical loss to encourage well-formed clusters. The online nature of PROTOCON allows it to utilise the label history of the entire dataset in one training cycle to refine labels in the following cycle without the need to store image embeddings. Hence, it can seamlessly scale to larger datasets at a low cost. Finally, PROTOCON addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss. It delivers significant gains and faster convergence over state-of-the-art across 5 datasets, including CIFARs, ImageNet and DomainNet. © 2023, CC BY-SA.

关键词： Embeddings