Public articles linked to the same research event.
arXiv Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.
Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.
Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.
Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.