I built a clean, well-structured deep learning pipeline using MONAI (Medical Open Network for AI) on a public abdominal ultrasound dataset.

The pipeline included:

proper subject-grouped train/validation splits

robust preprocessing

carefully decoded segmentation masks

sensible loss functions

consistent evaluation

And the model still struggled to learn.

The interesting part isn't that the model underperformed. What mattered was the diagnosis: a series of simple checks that traced the problem back to the dataset, not the model.

Those checks are useful far beyond medical imaging. They apply to almost any machine learning project.

If you're new to ML, this is a lesson worth carrying into every project: understand your data before you tune your model.

I set out to build a medical image segmentation tutorial.…