arXiv:2606.30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertainty due to unavoidable information loss. Without modeling this uncertainty, existing methods encode incomplete evidence into deterministic representations that appear plausible but lack reliability. In this regime, we propose a probabilistic representation framework that models representations as Gaussian distributions, where their mean captures task information and th
The increasing complexity and opacity of AI models in critical applications like medical imaging necessitate advanced uncertainty quantification to build trust and ensure reliability.
Improving the robustness and reliability of AI in medical diagnostics enhances patient safety and accelerates the adoption of AI in healthcare, potentially leading to earlier and more accurate diagnoses.
AI models for medical image analysis can now provide not just a segmentation but also a quantified confidence level, allowing clinicians to better understand the limits of AI predictions.
- · Medical AI developers
- · Healthcare providers
- · Patients with brain tumors
- · Diagnostic imaging companies
- · AI models without uncertainty quantification
More reliable AI-powered brain tumor segmentation assists radiologists in complex diagnostic tasks.
Increased trust in AI diagnostic tools leads to broader integration into clinical workflows and potentially reduces diagnostic errors.
The methodology could be adapted to other critical AI applications, accelerating robust AI deployment across various high-stakes domains.
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Read at arXiv cs.LG