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Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
Jue Jiang, Harini Veeraraghavan
Methods: Nine SSL methods spanning four pretext-task families were pretrained from scratch using the same 10{,}412 3D CT scans (1.89~M 2D axial slices) covering varied disease site…
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random…
Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy
Sudharsan Madhavan, Chengcheng Gui, Lando Bosma +8
Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained a…
Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma +7
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging…
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis
Jue Jiang, Aneesh Rangnekar, Chloe Min Seo Choi +1
Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted w…