mlsanityData to 13 Oct 2026

Challenges for machine learning in clinical translation of big data imaging studies

arXiv 2107.05630 ↗First posted 7 Jul 2021eess.IV cs.LGcited by 15

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Abstract (arXiv)

The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the success of deep learning when applied to many neuroimaging tasks, there remain barriers to the clinical translation of large-scale datasets and processing tools. Here, we explore the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation and logistical challenges, and discuss the challenges we believe are still to be overcome to enable the full success of big data deep learning approaches to be experienced outside of the research field.

Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 90 of its 212 references that are in the map. First posted on arXiv 7 Jul 2021.

Builds on

90 of 212 references.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 2; 2025: 4; 2026: 1.

15 papers.

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