IEE Transactions on Medical Imaging This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in FL use-cases where the clients’ training involves updating the Batch Normalization (BN) statistics and provide a new baseline attac...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

FedNI: Federated Graph Learning With Network Inpainting for Population-Based Disease Prediction
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to coll...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Brain Graph Evolution Prediction Using Decentralized Connectivity Datasets With Temporally-Varying Acquisitions
Foreseeing the evolution of brain connectivity between anatomical regions from a baseline observation can propel early disease diagnosis and clinical decision making. Such task becomes challenging when learning from multiple decentralized datasets with missing timepoints (e.g., datasets collected from different hospitals with a varying sequence of acquisitions). Federated learning (FL) is an emerging paradigm that enables collaborative learning among multiple clients (i.e., hospitals) in a fully privacy-preserving fashion. However, to the best of our knowledge, there is no FL work that foresees the time-dependent brain con...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Specificity-Preserving Federated Learning for MR Image Reconstruction
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the domain shift caused by different MR imaging protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information about the device properties and be useful for local reconstruction. In this paper, we propose a specificity-preserv...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Learning of Generative Image Priors for MRI Reconstruction
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling distributed training without transfer of imaging data. Existing FL methods employ conditional reconstruction models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the accelerated imaging operator. Since conditional models generalize poorly across different acceleration rates or sampling densities, imaging operators must be fixed between trainin...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images
Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access to patient-sensitive data in a distributed collaborative fashion. The federated approach relies on decentralized data distribution from various hospitals and clinics. The collaboratively learned global model is supposed to have acceptable performance for the individual sites. However, existing methods focus on minimizing the average of the aggregated loss functions, leading to...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN
Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized training on a large data set may be more reliable and more generalized, the slides to the examination are more often than not collected from many distributed medical institutes. This brings its own challenges, and the most important is the assurance of privacy and security of incoming data samples. In the discipline of histopathology image, the universal stain-variation issue adds ...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that the training set for each local client is annotated in a similar fashion and thus follows the same image supervision level. To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels. In FedMix, each client updates the federated model by integrating and effecti...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Partially Supervised Learning With Limited Decentralized Medical Images
Data government has played an instrumental role in securing the privacy-critical infrastructure in the medical domain and has led to an increased need of federated learning (FL). While decentralization can limit the effectiveness of standard supervised learning, the impact of decentralization on partially supervised learning remains unclear. Besides, due to data scarcity, each client may have access to only limited partially labeled data. As a remedy, this work formulates and discusses a new learning problem federated partially supervised learning (FPSL) for limited decentralized medical images with partial labels. We stud...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervis...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Cycling (FedCy): Semi-Supervised Federated Learning of Surgical Phases
Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expe...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models
Federated Learning (FL) could solve the challenges of training AI models on large datasets for medical imaging due to data privacy and ownership concerns by allowing collaborative training without the need for sharing raw data. This Special Issue on Federated Learning for Medical Imaging features papers covering FL-related topics and discussing their implications for healthcare and medical imaging. The included articles focus on a broad range of federated scenarios and applications, such as semi-supervised and self-supervised learning, histopathology, image reconstruction, graph neural networks, privacy preservation, activ...
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

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(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 1, 2023 Category: Biomedical Engineering Source Type: research

Unsupervised Medical Image Translation With Adversarial Diffusion Models
Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise...
Source: IEE Transactions on Medical Imaging - June 28, 2023 Category: Biomedical Engineering Source Type: research

Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation
Existing self-supervised medical image segmentation usually encounters the domain shift problem (i.e., the input distribution of pre-training is different from that of fine-tuning) and/or the multimodality problem (i.e., it is based on single-modal data only and cannot utilize the fruitful multimodal information of medical images). To solve these problems, in this work, we propose multimodal contrastive domain sharing (Multi-ConDoS) generative adversarial networks to achieve effective multimodal contrastive self-supervised medical image segmentation. Compared to the existing self-supervised approaches, Multi-ConDoS has the...
Source: IEE Transactions on Medical Imaging - June 28, 2023 Category: Biomedical Engineering Source Type: research