New AI methods make medical image analysis more reliable

Machine learning enables computers to learn from data and use that knowledge to make predictions or decisions. In health care, machine learning supports tasks such as disease diagnosis, predicting disease progression, treatment planning and patient monitoring.

“Applying machine learning to medical images is particularly challenging because datasets are often limited, while the models must be both reliable and interpretable,” says Disi Lin, a doctoral student at the Department of Computing Science at Umeå University.

In her doctoral research, Lin has addressed these challenges by integrating prior knowledge into machine learning models. This includes using mathematical morphology, a technique that can encourage learned patterns to form meaningful structures.

Classification of Alzheimer’s disease

One application area explored is the classification of Alzheimer’s disease.

“Alzheimer’s disease is associated with gradual structural changes in the brain, including reduced volume in brain regions that are important for memory and cognition,” says Lin.

Her research combines classical image mathematics with modern machine learning, resulting in models that can identify connected and anatomically meaningful regions more effectively than previous approaches.

“This makes it easier to relate the resulting patterns to disease-related changes and increases confidence in the assessment,” says Lin.

As part of her thesis, she has developed methods that take into account the fact that neighboring areas in an MRI scan often consist of the same type of tissue. This reduces the impact of random variations and makes the results more stable and reliable.

“I have also developed methods that not only provide a prediction but also indicate how confident the model is in its prediction. This allows clinicians to see which parts of the analysis are based on robust calculations and which are more uncertain,” says Lin.

AI with common sense

Lin has incorporated prior knowledge about image structure into machine learning models to help computers identify connected and spatially coherent regions. Rather than treating pixels independently, her AI models can focus on the most relevant areas of the brain, making the results more coherent, interpretable and medically meaningful.

“These methods can also show how certain the predictions are. This is important in health care because obtaining a result is not enough; clinicians also need to know how confident that result is,” says Lin.

Who’s behind this story?

Swati Mestri

Swati Mestri holds a bachelor’s degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space.

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Andrew Zinin

Master’s in physics with research experience. Long-time science news enthusiast. Plays key role in Science X’s editorial success.

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