Abstract:Purpose: Patient-specific quality assurance (PSQA) failures in radiotherapy can cause a delay in patient care and increase the workload and stress of staff. We developed a tabular transformer model based directly on the multi-leaf collimator (MLC) leaf positions (without any feature engineering) to predict IMRT PSQA failure in advance. This neural model provides an end-to-end differentiable map from MLC leaf positions to the probability of PSQA plan failure, which could be useful for regularizing gradient-ba… Show more
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