arXiv:2507.14760v2 Announce Type: replace-cross Abstract: While deep learning offers tremendous promise for scientific and medical imaging, any failures and hallucinations (predictions that do not coincide with reality) are hard to pinpoint and can have serious downstream consequences. Uncertainty estimation techniques, such as conformal prediction, can help by predicting statistically valid error bars for a model's prediction. However, popular conformal prediction methods were not designed for high-dimensional image-valued problems and do not take into account spatial correlations within an i
The increasing deployment of deep learning in critical imaging applications necessitates robust uncertainty quantification methods to ensure reliability and trust.
This research addresses a critical limitation in current AI applications, particularly in areas like medical diagnostics and scientific imaging, where prediction errors carry significant consequences.
The ability to provide statistically valid, spatially aware error bars for high-dimensional image predictions will improve the trustworthiness and utility of AI in sensitive fields.
- · Medical imaging software developers
- · Scientific research institutions
- · AI safety and reliability companies
- · Patients receiving AI-assisted diagnoses
- · AI models lacking uncertainty quantification
- · Legacy image analysis methods
- · Developers ignoring AI safety
- · Institutions reliant on black-box AI
Improved reliability and adoption of AI in high-stakes imaging applications.
Increased regulatory scrutiny and standardization requirements for AI systems with safety-critical functions.
Enhanced public trust in AI technology, leading to broader integration across various societal sectors.
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Read at arXiv cs.LG