How to reduce false positives in medical image segmentation using U-Net and Focal Loss?

All answers to this question.

For medical segmentation with extreme imbalance, Tversky Loss is often superior to standard Dice or Focal Loss. Tversky Loss allows you to explicitly weight false positives versus false negatives by adjusting the alpha and beta parameters. In your case, increasing the penalty for false positives will force the U-Net to be more "conservative." Another technique we used in a 2023 study was "Attention U-Net," which adds attention gates to the skip connections. This helps the decoder focus on relevant spatial features passed from the encoder, effectively filtering out noise in the background areas that often leads to those pesky false detections.

   Answered 2025-03-20 by Jennifer Williams

Have you tried a two-stage approach where the first model identifies the "Region of Interest" (ROI) and the second model does the fine-grained segmentation only on that crop?

   Answered 2025-04-05 by Michael Brown

• Michael, that is a very robust strategy for medical AI. By cropping the image to a 64x64 or 128x128 patch around the potential lesion, you effectively change the class balance from 1% to maybe 30%. This makes the optimization landscape much easier for the second U-Net to navigate and significantly reduces the chance of the model picking up artifacts in distant parts of the scan.

     Commented 2025-04-15 by Thomas Harris

Deep Supervision is another trick—adding auxiliary loss functions at different scales of the U-Net can help the model learn more discriminative features faster.

   Answered 2025-04-25 by Amanda Clark

• Spot on, Amanda. Deep supervision really helps with the vanishing gradient problem in deeper U-Net variants, especially when the target features are so small and sparse.

     Commented 2025-04-30 by Samantha Reed