Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

• URL: http://arxiv.org/abs/2306.16367v1
• Date: Wed, 28 Jun 2023 17:00:32 GMT
• Title: Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
• Authors: Won Joon Yun, Samuel Kim, Joongheon Kim
• Abstract summary: This paper develops an integrated framework that addresses data privacy and regulatory compliance challenges. It includes the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and substantiating the efficacy of the proposed approach.
• Score: 13.176351544342735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach.

Related papers

• Federated GNNs for EEG-Based Stroke Assessment [1.3274340213871945]
  This study proposes a novel method that combines federated learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity. Our approach enables multiple hospitals to jointly train a shared GNN model on their local EEG data without exchanging patient information.
  arXiv  Detail & Related papers  (2024-11-04T17:13:35Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare [14.399086205317358]
  Foundation models (FMs) are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback. These models are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data.
  arXiv  Detail & Related papers  (2024-05-10T19:22:24Z)
• Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models [29.05425041393475]
  Generative Large Language Models (LLMs) hold significant promise in healthcare. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center.
  arXiv  Detail & Related papers  (2024-01-05T15:09:57Z)
• README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP [9.432205523734707]
  We introduce a new task of automatically generating lay definitions, aiming to simplify medical terms into patient-friendly lay language. We first created the dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality.
  arXiv  Detail & Related papers  (2023-12-24T23:01:00Z)
• An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology [1.2714439146420664]
  We present state-of-the-art NLP applications that employ large language models (LLMs) in radiation oncology research. LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.
  arXiv  Detail & Related papers  (2023-11-03T19:32:35Z)
• Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models [48.07083163501746]
  Clinical natural language processing requires methods that can address domain-specific challenges. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our empirical study across 7 clinical NLP tasks and 16 datasets reveals that ClinGen consistently enhances performance across various tasks.
  arXiv  Detail & Related papers  (2023-11-01T04:37:28Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.