Distributed Skin Lesion Analysis Across Decentralised Data Sources

Mou, Yongli; Welten, Sascha; Jaberansary, Mehrshad; Yediel, Yeliz Ucer; Kirsten, Toralf; Decker, Stefan; Beyan, Oya

doi:10.3233/shti210179

Cited by 6 publications

(8 citation statements)

References 9 publications

(11 reference statements)

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“… Our evaluation involved RDF data structures in the user-study and three application scenarios: International Skin Image Collaboration (ISIC)-GEN, ISIC-SAMPLE, and the Breast Cancer (BC) use case. The user-study used two distributed data sources with synthetic data, while ISIC-GEN used three data sources with synthetic data, and ISIC-SAMPLE used one data source with real sample data ( 13 ). Lastly, the BC use case leveraged six data sources with real sample data ( 14 ).…”

Section: Resultsmentioning

confidence: 99%

“…Our initial step involves examining how the analysis code operates on a conceptual and abstract level. In general, two execution policies exist that enable DA: A parallel and a sequential approach (sometimes referred to as FL and Institutional Incremental Learning (IIL), respectively) ( 13 , 29 ). In IIL, the data holders are arranged in a sequence, and the analysis code is sent from institution to institution until the last institution sends the final (and aggregated) results back.…”

Section: Methodsmentioning

confidence: 99%

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Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

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The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

Self Cite

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“…This has the advantage that the analysis code is programming language independent and hence increases the flexibility. The PHT has already been applied to several data use cases in the healthcare domain, such as skin lesion analysis, radiomics, or lung cancer [14,15,24]. As all the above-mentioned methods foster collaborative data sharing, there is the indispensable necessity to technologically on-board each participating party and enable access to the network.…”

Section: Distributed Analyticsmentioning

confidence: 99%

Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics

et al. 2022

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The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

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“…The artefacts of the model have been made available online (refer to the Supplementary Data). Another use case study-including lightweight statistical analysis and complex ML tasks-using our architecture can be found in the work of Mou et al 12 Fig. 6 Encryption/Decryption workflow.…”

Section: Analytical Tasksmentioning

confidence: 99%

Untitled

2022

Methods Inf Med

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Background In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. Objective We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. Methods In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. Results We show that our infrastructure enables the training of data models based on distributed data sources. Conclusion Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

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Distributed Skin Lesion Analysis Across Decentralised Data Sources

Cited by 6 publications

References 9 publications

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics

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