MuellerPT pretrains on Lu-Chipman map prediction from Mueller matrices with the MAP-Org dataset, yielding over 20% DICE gain in 5%-label brain segmentation and 8% accuracy gain in 1%-label cancer classification versus scratch baselines.
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Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.
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MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry
MuellerPT pretrains on Lu-Chipman map prediction from Mueller matrices with the MAP-Org dataset, yielding over 20% DICE gain in 5%-label brain segmentation and 8% accuracy gain in 1%-label cancer classification versus scratch baselines.
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Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.