Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

• URL: http://arxiv.org/abs/2111.09461v1
• Date: Thu, 18 Nov 2021 00:43:41 GMT
• Title: Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
• Authors: Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Sch\"onlieb, Tian Xia
• Abstract summary: We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
• Score: 79.038671794961
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining [12.656718786788758]
  We propose a self-conditioned diffusion model termed DiffChest and train it on a dataset of chest radiographs. DiffChest explains classifications on a patient-specific level and visualizes the confounding factors that may mislead the model. Our findings highlight the potential of pretraining based on diffusion models in medical image classification.
  arXiv  Detail & Related papers  (2023-09-29T10:38:08Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study [1.5308395762165423]
  We develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.
  arXiv  Detail & Related papers  (2021-09-01T04:46:27Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans [8.694504007704994]
  COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world. We propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans.
  arXiv  Detail & Related papers  (2021-01-28T11:47:35Z)
• Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 [1.1764495014312295]
  COVID-19 pandemic continues to spread and impact the well-being of the global population. Front-line modalities including computed tomography (CT) and X-ray play an important role for triaging COVID patients. Considering the limited access of resources (both hardware and trained personnel) and decontamination considerations, CT may not be ideal for triaging suspected subjects.
  arXiv  Detail & Related papers  (2020-11-10T15:36:08Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification [45.86448200141968]
  We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We propose a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in
  arXiv  Detail & Related papers  (2020-06-02T08:05:06Z)

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.