@arunvenkatadri.bsky.social
High AUC alone isn’t enough — generalization, false positives, and interpretability matter too.
But matching radiologist-level performance on complex dynamic MRI data is a massive leap toward AI-augmented healthcare.
#HealthTech #AIforGood #ICLR2025
But matching radiologist-level performance on complex dynamic MRI data is a massive leap toward AI-augmented healthcare.
#HealthTech #AIforGood #ICLR2025
April 26, 2025 at 11:53 AM
High AUC alone isn’t enough — generalization, false positives, and interpretability matter too.
But matching radiologist-level performance on complex dynamic MRI data is a massive leap toward AI-augmented healthcare.
#HealthTech #AIforGood #ICLR2025
But matching radiologist-level performance on complex dynamic MRI data is a massive leap toward AI-augmented healthcare.
#HealthTech #AIforGood #ICLR2025
Quick primer:
AUC (Area Under the ROC Curve) measures a model’s ability to distinguish between classes (e.g., cancer vs. no cancer).
An AUC of 1.0 = perfect classification.
0.5 = random guessing.
93.9% AUC = ~94% correct ranking — elite diagnostic performance.
AUC (Area Under the ROC Curve) measures a model’s ability to distinguish between classes (e.g., cancer vs. no cancer).
An AUC of 1.0 = perfect classification.
0.5 = random guessing.
93.9% AUC = ~94% correct ranking — elite diagnostic performance.
April 26, 2025 at 11:52 AM
Quick primer:
AUC (Area Under the ROC Curve) measures a model’s ability to distinguish between classes (e.g., cancer vs. no cancer).
An AUC of 1.0 = perfect classification.
0.5 = random guessing.
93.9% AUC = ~94% correct ranking — elite diagnostic performance.
AUC (Area Under the ROC Curve) measures a model’s ability to distinguish between classes (e.g., cancer vs. no cancer).
An AUC of 1.0 = perfect classification.
0.5 = random guessing.
93.9% AUC = ~94% correct ranking — elite diagnostic performance.