DAGMaN uses co-distilled attention-guided masked image modeling with a noisy teacher to enable effective self-supervised pretraining on medical images by selective masking of co-occurring patches and maintenance of attention head diversity, with demonstrations on nodule classification, immunotherapy
We fine-tuned our model on this dataset (350 for training and 27 used for validation), and then tested on the LIDC containing lung nodules (Ar- mato III et al., 2011)
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Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
DAGMaN uses co-distilled attention-guided masked image modeling with a noisy teacher to enable effective self-supervised pretraining on medical images by selective masking of co-occurring patches and maintenance of attention head diversity, with demonstrations on nodule classification, immunotherapy