Improved outcome models with denoising diffusion

Over the last few years, machine learning models have dominated in the area of treatment outcome modeling thanks to their superior performance, interpretation, and possibility to combine a variety of input data (e.g., imaging data, treatment planning data, multi-omics data, patient demographics) [1 –7]. Unfortunately, many modeled endpoints (e.g., local control, regional/distant recurrence or radiation toxicities) are often distributed sparsely, with resultant class imbalance in the dataset [8–12].

Source: Physica Medica: European Journal of Medical Physics - February 6, 2024 Category: General Medicine Authors: D. Dudas, T.J. Dilling, I. El Naqa Source Type: research