Computer Vision & Deep Learning for Medical Image Segmentation

Abstract

We present a segmentation framework for multi-institution medical imaging that achieves 94% diagnostic agreement with expert radiologists across heterogeneous scanner sources. The work addresses the central obstacle to clinical AI adoption: models that perform well on a single institution’s data degrade sharply when deployed elsewhere. Our approach combines domain-adaptive normalization with uncertainty-aware segmentation to produce predictions clinicians can calibrate their trust against.

Problem

Medical image segmentation models are typically trained and validated on data from a single site, with a single scanner profile and annotation protocol. In deployment, distribution shift—different machines, acquisition settings, and patient populations—causes silent accuracy loss precisely where errors are most costly.

Approach

Domain-adaptive normalization to align feature statistics across imaging sources without requiring labeled target data
Uncertainty-aware segmentation that flags low-confidence regions for human review rather than producing overconfident masks
Multi-site validation across institutions to measure real generalization rather than in-distribution performance

Results

94% diagnostic agreement with expert consensus across multi-institution test sets
Substantial reduction in cross-site performance drop compared to standard U-Net baselines
Calibrated uncertainty maps that correlate with regions of expert disagreement

Why It Matters

In diagnostic settings, an AI system that is confidently wrong is more dangerous than one that abstains. This research treats reliability and calibration as first-class objectives, not afterthoughts—the same principle that anchors every curriculum I deliver on responsible AI deployment.

Resources

Code, datasets, and the full preprint are available through the links above.