ST-FL: style transfer preprocessing in federated learning for COVID-19 segmentation

  title={ST-FL: style transfer preprocessing in federated learning for COVID-19 segmentation},
  author={Antonios Georgiadis and Varun Babbar and Fran Silavong and Sean J. Moran and Rob den Otter},
  booktitle={Medical Imaging},
Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. Most hospitals or countries can train their own models using in-house data, however empirical evidence shows that those models perform poorly when tested on new unseen cases, surfacing the need for coordinated global collaboration. Due to privacy regulations, medical data sharing… 

A Survey on Deep Learning in COVID-19 Diagnosis

The latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network, one of the most widely used machine learning methods, are introduced.

Towards Federated COVID-19 Vaccine Side Effect Prediction

An adaptive approach to fuse heterogeneous EHR data and apply data augmentation techniques working with a margin loss to overcome the data imbalance issue in the client model training is presented to address both challenges simultaneously in FedCovid.

Federated Domain Generalization: A Survey

  • Ying LiXingwei Wang Schahram Dustdar
  • Computer Science
  • 2023
This paper discusses the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as providing the corresponding formal definition, and categorizes recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization.

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

A consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images, and yields significant improvement over conventional point-level loss functions and almost matches the performance of models trained with full supervision with much less human effort.

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks

It is shown that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data, which will be valuable to medical imaging researchers to reduce manual segmentation effort and cost in CT imaging.

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

This research presents a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN and demonstrates that the synthetic images produced by this model can be utilized to enhance the performance of CNN for COVID-19 detection.

Unpaired Image Denoising via Wasserstein GAN in Low-Dose CT Image with Multi-Perceptual Loss and Fidelity Loss

A generative adversarial network combining multi-perceptual loss and fidelity loss is proposed, which performs comparably to the current deep learning methods which utilize paired image pairs.

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

It is shown that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and the effects of data distribution across collaborating institutions on model quality and learning patterns are investigated.

Federated Semi-Supervised Learning with Inter-Client Consistency

FedMatch improves upon naive federated semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters into parameters for labeled and unlabeled data.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

A framework that collects a small amount of data from different sources and trains a global deep learning model using blockchain-based federated learning and uses Capsule Network-based segmentation and classification to detect COVID-19 patients and designs a method that can collaboratively train a global model using Blockchain technology with Federated learning while preserving privacy.

Image-to-Image Translation with Conditional Adversarial Networks

Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

Learning to denoise astronomical images with U-nets

Astronomical images are essential for exploring and understanding the universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope, are heavily oversubscribed in the