MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
arXiv preprint arXiv:2404.09556 , year=
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nnU-Net with ResNet encoder, intensity normalization, batch dice loss, and CraveMix augmentation reaches Dice 0.80 and third place in AutoPET III.
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.
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MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
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Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III
nnU-Net with ResNet encoder, intensity normalization, batch dice loss, and CraveMix augmentation reaches Dice 0.80 and third place in AutoPET III.
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AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.