Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

11/23/2020
by   Dong Yang, et al.
5

The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing.

READ FULL TEXT

page 1

page 5

page 12

page 17

06/22/2020

Federated Semi-Supervised Learning with Inter-Client Consistency

While existing federated learning approaches mostly require that clients...
04/07/2021

Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

The novel coronavirus disease 2019 (COVID-19) characterized by atypical ...
05/03/2021

Noisy Student learning for cross-institution brain hemorrhage detection

Computed tomography (CT) is the imaging modality used in the diagnosis o...
04/07/2018

Semi-supervised multi-organ segmentation via multi-planar co-training

Multi-organ segmentation is a critical problem in medical image analysis...
02/26/2020

A Survey towards Federated Semi-supervised Learning

The success of Artificial Intelligence (AI) should be largely attributed...
12/09/2021

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images

The world is currently experiencing an ongoing pandemic of an infectious...
10/23/2019

Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

The diagnosis of the presence of metastatic lymph nodes from abdominal c...