Slide-Level Uncertainty Quantification For Deep Learning Predictions In Digital Histopathology.
SUMMARY
The technology improves diagnostic accuracy in digital histopathology by providing slide-level uncertainty quantification for deep learning predictions, enabling more reliable and actionable clinical decision-making in oncology.
The Unmet Need: Reliable quantification of prediction uncertainty in deep learning for digital histopathology
- Deep learning models have become integral in digital histopathology for tumor classification and prognosis, yet existing approaches lack robust estimation of predictive uncertainty at the slide level, limiting clinical trust and potentially leading to misdiagnoses or inappropriate treatments. Current methods struggle with domain shifts and varying data quality, impeding reliable interpretation of model outputs.
- The broader trend toward integrating artificial intelligence in medical diagnostics highlights the critical need for techniques that transparently quantify model confidence, especially in multi-institutional settings, as healthcare systems demand dependable AI tools that operate reliably across diverse patient
populations and data environments.
The Proposed Solution: Slide-level uncertainty quantification method using nested cross-validation and thresholding strategy for deep learning models in histopathology
- The faculty inventor developed a novel computational framework that estimates prediction uncertainty for whole-slide images through dropout-based methods combined with a nested cross-validation strategy that defines low- and high-confidence thresholds immune to validation data leakage. This approach provides actionable uncertainty metrics at the patient level and has been validated externally on two large, multi-institutional datasets, demonstrating robustness against domain shifts. Unlike prior
methods, it specifically targets slide-level uncertainty, incorporates assessment of required training data volume for reliable estimates, and is designed for clinical applicability in oncology diagnostics as part of a histology microscope platform currently under development.
ADVANTAGES
- Accurate slide-level uncertainty quantification
- Robust thresholding immune to data leakage
- Validated on multi-institutional large datasets
- Quantification tailored for clinical decision support
- Assessment of training data requirements for reliability
- Integration with histology microscope hardware-software system
APPLICATIONS
- Cancer diagnostics and tumor classification in digital histopathology
- Prognostic evaluation in oncology clinical workflows
- Quality control for AI-based pathology decision support tools