Artificial intelligence studies are increasingly common in radiology, pathology, dermatology, and orthopedics. You also want to "teach" your images to an AI model, but you do not know how deep learning works or where to start. This guide answers these questions in non-technical language.
Why Deep Learning for Image Analysis?
Classical machine learning methods work on hand-picked features: for example, "if this pixel's intensity is at this value, there is a nodule." This approach is very limited for medical images.
Deep learning, and especially the CNN (Convolutional Neural Network), extracts the features it will learn from the data itself. When shown a sufficient number of labeled images, it discovers which features determine the diagnosis.
How Does a CNN Work, Simply?
A CNN processes the image in layers. Each layer captures different features of the image; the first layers learn edges and color transitions, the next layers learn more complex patterns, and the deepest layers learn class-specific information.
During training, the model first makes random guesses, then measures its errors and updates its weights. This process continues over thousands of images and thousands of iterations.
Transfer Learning: How to Work With Little Data?
Labeling is hard and expensive for medical images. Collecting thousands of labeled X-rays can take months. Transfer learning largely solves this problem.
A model trained on the ImageNet dataset (14 million nature photos), such as ResNet, EfficientNet, or VGG, already possesses basic image recognition capabilities. If you take this model and perform "fine-tuning" with a small number of medical images, you achieve much better results compared to training from scratch.
How Many Images Do You Need?
There is no definite answer to this question; it varies according to the task, whether the disease is rare or common, and whether transfer learning is used.
Roughly:
- Classification (disease X/Y present/absent): 500-1000 images per class with transfer learning may be sufficient
- Segmentation (drawing boundaries around organs): usually requires more images and annotation
- Multi-class classification: as the number of classes increases, the data requirement increases
For realistic planning, a data adequacy analysis should be performed during the study design phase.
Validation: The Most Critical Step
Internal validation (setting aside a portion of your own data for testing) is necessary as a start but is not sufficient.
External validation: Tests how the model performs on images obtained from another hospital and a different imaging device. Without this step, it is unknown whether the model works in the real world.
High-impact journals do not accept a medical AI paper without external validation.
Ethics Committee and KVKK
Images cannot be fed into the model without being anonymized. DICOM files contain patient information; a preprocessing step that removes patient identity from the metadata is mandatory.
Get technical support for your medical imaging AI study.
Where Do People Get Stuck Most in This Analysis?
- You do not know how many images you need, and it is unclear whether the collected data is sufficient.
- There is no annotation protocol, two experts label differently, and inter-rater agreement is low.
- The model performs well, but you cannot submit it to a journal without external validation.