Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism

• URL: http://arxiv.org/abs/2210.16142v1
• Date: Fri, 28 Oct 2022 14:12:42 GMT
• Title: Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
• Authors: Yiqing Shen, Baiyun Liu, Ruize Yu, Yudong Wang, Shaokang Wang, Jiangfen Wu, Weidao Chen
• Abstract summary: Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Recent advance in deep learning (DL) has shown their promising potential in COPD identification from CT images. We propose a novel personalized federated learning (PFL) method based on vision transformer (ViT) for distributed and heterogeneous COPD CTs.
• Score: 1.762550832378922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination as well as functional airway limitation, which may cause a considerable portion of COPD patients underdiagnosed especially at the early stage. Recent advance in deep learning (DL) has shown their promising potential in COPD identification from CT images. However, with heterogeneous syndromes and distinct phenotypes, DL models trained with CTs from one data center fail to generalize on images from another center. Due to privacy regularizations, a collaboration of distributed CT images into one centralized center is not feasible. Federated learning (FL) approaches enable us to train with distributed private data. Yet, routine FL solutions suffer from performance degradation in the case where COPD CTs are not independent and identically distributed (Non-IID). To address this issue, we propose a novel personalized federated learning (PFL) method based on vision transformer (ViT) for distributed and heterogeneous COPD CTs. To be more specific, we partially personalize some heads in multiheaded self-attention layers to learn the personalized attention for local data and retain the other heads shared to extract the common attention. To the best of our knowledge, this is the first proposal of a PFL framework specifically for ViT to identify COPD. Our evaluation of a dataset set curated from six medical centers shows our method outperforms the PFL approaches for convolutional neural networks.

Related papers

• FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
  Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
  arXiv  Detail & Related papers  (2024-10-28T02:24:01Z)
• FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography [0.3823356975862007]
  coronary stenosis is an underlying factor in heart failure. Current practice for grading coronary lesions suffers from several drawbacks. Deep learning methods have emerged to assist cardiologists in automating the estimation of FFR/iFR values.
  arXiv  Detail & Related papers  (2024-09-21T23:52:05Z)
• cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations [0.6733204971296001]
  We propose cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC.
  arXiv  Detail & Related papers  (2023-07-14T10:05:37Z)
• Federated Learning with Privacy-Preserving Ensemble Attention Distillation [63.39442596910485]
  Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage.
  arXiv  Detail & Related papers  (2022-10-16T06:44:46Z)
• UniToBrain dataset: a Brain Perfusion Dataset [2.02258267891574]
  We present UniToBrain, the very first open-source dataset for "perfusion maps" We propose a novel neural networks-based algorithm, using the European library ECVL and EDDL for the image processing and developing deep learning models respectively.
  arXiv  Detail & Related papers  (2022-08-01T07:16:02Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)
• Differentially private federated deep learning for multi-site medical image segmentation [56.30543374146002]
  Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer. Recent initiatives have demonstrated that segmentation models trained with FL can achieve performance similar to locally trained models. However, FL is not a fully privacy-preserving technique and privacy-centred attacks can disclose confidential patient data.
  arXiv  Detail & Related papers  (2021-07-06T12:57:32Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• COPD Classification in CT Images Using a 3D Convolutional Neural Network [10.217631381481457]
  Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. We propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
  arXiv  Detail & Related papers  (2020-01-04T16:58:45Z)

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.