Why Your Deep Learning Model Isn't Learning: Diagnosing Data Problems in Medical Imaging

                     Why Your Deep Learning Model Isn't Learning: Diagnosing Data Problems in Medical Imaging
               



Tópico:
                     Why Your Deep Learning Model Isn't Learning: Diagnosing Data Problems in Medical Imaging
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

Conteúdo do Tutorial / Guia Passo a Passo:
-------------------------------------------------------------------------
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. I ended up learning a more valuable lesson: no amount of careful engineering can rescue a model from a dataset that can't support the task.

By the end of this article, you'll understand:

• How to evaluate whether a dataset can actually support your task

• Why "the model isn't learning" is often a data problem

• How to rule out engineering bugs before blaming the data

• Practical diagnostics you can run in minutes

• Why synthetic training data often struggles in real-world deployment

• When to stop tuning and walk away from a dataset

This is not a beginner introduction to deep learning – it assumes familiarity with concepts like UNet architectures and training loops. But the data-quality lessons apply broadly to many ML projects.

What We'll Cover:

• The Dataset

• Step 1: Rule Out the Pipeline Before Blaming the Data

• Subject-grouped splits

• Decoding masks correctly

• Loss design and class weighting

• Step 2: The Model Still Struggled

• Step 3: Interrogating the Dataset

• Diagnostic 1: What Does the Dataset Actually Contain?

• Diagnostic 2: Do Synthetic and Real Images Look Similar?

• Diagnostic 3: Can the gap be fixed by adding real data?

• Step 4: Knowing When to Stop

• A Practical Dataset Evaluation Checklist

• What I Would Try Next

• The Bigger Lesson

The Dataset

I used the US Simulation & Segmentation dataset, a public collection of abdominal ultrasound images with organ segmentation labels from Kaggle.

It contains:

• 926 synthetic ultrasound images — generated by a ray-casting simulator from CT scans, with full organ annotations

• 617 real ultrasound images — from an actual ultrasound scanner

• Labels for 8 organs — liver, kidney, gallbladder, pancreas, spleen, bones, vessels, and adrenals

At first glance, the dataset looked ideal:

• thousands of images

• multiple organ classes

• both synthetic and real ultrasound data

Whether it actually supported the task was a different question.

Step 1: Rule Out the Pipeline Before Blaming the Data

Ground rule: you should always rule out the pipeline before blaming the data. A model failing on buggy code looks exactly like a model failing on bad data. The engineering needs to be trustworthy.

Subject-Grouped Splits

A common mistake in medical imaging is randomly splitting images into train and test sets.

That approach is problematic because many frames come from the same patient. Those frames share anatomy, scanner settings, and noise patterns.

If frames from the same patient appear in both the train and test sets, the model can partially memorize patient-specific patte

... [O tutorial continua no link abaixo] ...