"I want to do something with AI on my X-rays", this request is a correct but incomplete starting point. In medical imaging AI studies, the most important first decision is determining which AI approach will answer the clinical question. When the wrong approach is chosen, the data collected over months, the labels prepared, and the code written can go entirely to waste.
Three Fundamental Approaches and Their Clinical Meanings
Classification: A single label is assigned to the entire image. It answers questions such as "Is there pneumonia in this X-ray or not?" or "What grade is the tumor in this MRI?"
Clinical equivalent: Screening and triage. Prioritization for radiological review, distinguishing an urgent case from a normal image.
Detection: The location and size of a specific object in the image is determined. "Is there lymphadenopathy in this X-ray, where, and how large?"
Clinical equivalent: Localization of a pathological finding. Surgical intervention planning for the surgeon, treatment area determination for the radiation specialist.
Segmentation: It is determined which anatomical structure or pathology each pixel belongs to. Bone boundaries, tumor boundaries, organ volumes.
Clinical equivalent: Volumetric measurement, anatomical planning, 3D reconstruction. Patient-specific surgical guide design, radiation planning, volumetric follow-up.
Why Is the Wrong Choice Made?
The most common mistake we see is this: The researcher wants to do segmentation because it looks like a "complete and detailed analysis", but the clinical question can actually be answered with classification.
Segmentation requires far more labeling effort, a larger dataset, and a more complex model than classification. If the clinical question is "Is there pathology in this image?", segmentation is both unnecessary and expensive.
The reverse is also true: If the surgeon wants to know the exact boundary of the tumor, classification does not answer it. Saying "there is a tumor" is not enough; where it begins and where it ends is required.
The Choice of Approach Affects Data Collection Decisions
This decision must be made at the beginning of the research, because each approach requires a different labeling protocol.
For classification, an image-level label is sufficient: "pathological / normal." A specialist can classify hundreds of images within minutes.
For segmentation, a pixel-level label is required. For each image, the specialist has to draw the structure boundaries, and this takes hours rather than minutes. The cost and duration of data collection increase dramatically.
Once labeling has started, changing the approach is not possible. Classification labels cannot be converted into segmentation.
Hybrid Approaches and Their Complexities
Some clinical questions cannot be answered with a single approach. For example: The question "Is this tumor malignant and what is its volume?" requires both classification and segmentation.
In this case, will two separate models be developed, or a multi-task model? Each option has a different methodological, data, and computational cost.
Where Do People Get Stuck in This Process?
Researchers struggle most at these points: Translating the clinical question into a technical task definition. Anticipating the data collection requirements of the chosen approach. Deciding on a multi-task or cascade model. Evaluating the approach in terms of publishability.
To determine the approach of your medical imaging AI study together, request a 30-minute free consultation.
Where Do People Get Stuck Most in This Analysis?
- You chose segmentation, but pixel-level annotation is very expensive and time-consuming, whereas classification might have been sufficient.
- You did classification, but the reviewer says "show the exact localization of the lesion", and segmentation is also required.
- You are trying both approaches, but the comparison metric is different (AUC vs Dice), and it is unclear which one you will report.