Secure Federated Learning Approaches to Diagnosing COVID-19

• URL: http://arxiv.org/abs/2401.12438v1
• Date: Tue, 23 Jan 2024 02:14:05 GMT
• Title: Secure Federated Learning Approaches to Diagnosing COVID-19
• Authors: Rittika Adhikari, Christopher Settles
• Abstract summary: This paper introduces a HIPAA-compliant model to aid in the diagnosis of COVID-19. Federated learning is a distributed machine learning approach that allows for algorithm training across multiple decentralized devices. To enhance hospital understanding of the model, we employed a visualization technique that highlights key features in chest X-rays indicative of a positive COVID-19 diagnosis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent pandemic has underscored the importance of accurately diagnosing COVID-19 in hospital settings. A major challenge in this regard is differentiating COVID-19 from other respiratory illnesses based on chest X-rays, compounded by the restrictions of HIPAA compliance which limit the comparison of patient X-rays. This paper introduces a HIPAA-compliant model to aid in the diagnosis of COVID-19, utilizing federated learning. Federated learning is a distributed machine learning approach that allows for algorithm training across multiple decentralized devices using local data samples, without the need for data sharing. Our model advances previous efforts in chest X-ray diagnostic models. We examined leading models from established competitions in this domain and developed our own models tailored to be effective with specific hospital data. Considering the model's operation in a federated learning context, we explored the potential impact of biased data updates on the model's performance. To enhance hospital understanding of the model's decision-making process and to verify that the model is not focusing on irrelevant features, we employed a visualization technique that highlights key features in chest X-rays indicative of a positive COVID-19 diagnosis.

Related papers

• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Instrumental Variable Learning for Chest X-ray Classification [52.68170685918908]
  We propose an interpretable instrumental variable (IV) learning framework to eliminate the spurious association and obtain accurate causal representation. Our approach's performance is demonstrated using the MIMIC-CXR, NIH ChestX-ray 14, and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-05-20T03:12:23Z)
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection [2.1930130356902207]
  Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. We present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images.
  arXiv  Detail & Related papers  (2021-10-26T15:09:00Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19 [12.584626589565522]
  Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful. In this work we develop a novel CXR image retrieval model based on deep metric learning.
  arXiv  Detail & Related papers  (2020-11-26T03:16:48Z)
• Pinball-OCSVM for early-stage COVID-19 diagnosis with limited posteroanterior chest X-ray images [3.4935179780034247]
  This research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM) that can work in presence of limited COVID-19 positive CXR samples. The performance of the proposed model is compared with conventional OCSVM and existing deep learning models, and the experimental results prove that the proposed model outperformed over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-16T02:34:15Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)

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.