AI Technical Considerations:Data Storage, Cloud usage and AI Pipeline

Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access to these data in a standardized way. This requires careful design and implementation based on the current standards and guidelines and complying with the current legal restrictions. However, the realization of proper imaging data collections is not sufficient to train, validate and deploy AI as resource demands are high and require a careful hybrid implementation of AI pipelines both on-premise and in the cloud. This chapter aims to help the reader when technical considerations have to be made about the AI environment by providing a technical background of different concepts and implementation aspects involved in data storage, cloud usage, and AI pipelines

A Comparative Study of Federated Learning Models for COVID-19 Detection

Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-preserving manner. Deploying FL is not always feasible as it requires high computation and network communication resources. This paper evaluates five FL algorithms' performance and resource efficiency for Covid-19 detection. A decentralized setting with CNN networks is set up, and the performance of FL algorithms is compared with a centralized environment. We examined the algorithms with varying numbers of participants, federated rounds, and selection algorithms. Our results show that cyclic weight transfer can have better overall performance, and results are better with fewer participating hospitals. Our results demonstrate good performance for detecting COVID-19 patients and might be useful in deploying FL algorithms for covid-19 detection and medical image analysis in general

Use of a Thin-Section Archive and Enterprise 3-Dimensional Software for Long-Term Storage of Thin-Slice CT Data Sets—A Reviewers’ Response

Current developments in storage solutions, PACS, and client-server systems allow for 3D imaging at the desktop. This can be achieved together with full storage into PACS of all slices, including the very large thin-section CT datasets. This paper describes a possible setup, which has been in operation for several years now, in response to an article by Meenan et al. previously published in this journal (1)

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The central aim is to defend the network against potential privacy breaches while maintaining model robustness against adversarial manipulations. We show that incorporating distributed noise, grounded in the privacy guarantees in federated settings, enables the development of a adversarially robust model that also meets federated privacy standards. We conducted comprehensive evaluations across diverse attack scenarios, parameters, and use cases in cancer imaging, concentrating on pathology, meningioma, and glioma. The results reveal that the incorporation of distributed noise allows for the attainment of security levels comparable to those of conventional adversarial training while requiring fewer retraining samples to establish a robust model

Free DICOM de-identification tools in clinical research:functioning and safety of patient privacy

To compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header. Ten DICOM toolkits were selected for de-identification tests. Tests were performed by using the system's default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization. Only one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification. Only five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed. aEuro cent Free DICOM toolkits should be carefully used to prevent patient identity disclosure. aEuro cent Each DICOM tool produces its own specific outcomes from the de-identification process. aEuro cent In case optimal security is required, using one DICOM toolkit is proposed

Federated Learning in Medical Imaging:Part II: Methods, Challenges, and Considerations

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging due to the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them

Federated Learning in Medical Imaging:Part I: Toward Multicentral Health Care Ecosystems

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging