AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities

• URL: http://arxiv.org/abs/2504.03589v1
• Date: Fri, 04 Apr 2025 16:57:06 GMT
• Title: AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities
• Authors: Badhan Kumar Das, Gengyan Zhao, Han Liu, Thomas J. Re, Dorin Comaniciu, Eli Gibson, Andreas Maier,
• Abstract summary: We propose an adaptive Vision Transformer (AdaViT) framework capable of handling variable set of input modalities for each case.<n>We demonstrate that this architecture effectively transfers supervised pretrained models to new datasets with different input modality/contrast sets.<n>For self-supervised pretrain, the proposed method is able to maximize the pretrain data and facilitate transferring to diverse downstream tasks with variable sets of input modalities.
• Score: 9.006543373916314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrain techniques, whether supervised or self-supervised, are widely used in deep learning to enhance model performance. In real-world clinical scenarios, different sets of magnetic resonance (MR) contrasts are often acquired for different subjects/cases, creating challenges for deep learning models assuming consistent input modalities among all the cases and between pretrain and finetune. Existing methods struggle to maintain performance when there is an input modality/contrast set mismatch with the pretrained model, often resulting in degraded accuracy. We propose an adaptive Vision Transformer (AdaViT) framework capable of handling variable set of input modalities for each case. We utilize a dynamic tokenizer to encode different input image modalities to tokens and take advantage of the characteristics of the transformer to build attention mechanism across variable length of tokens. Through extensive experiments, we demonstrate that this architecture effectively transfers supervised pretrained models to new datasets with different input modality/contrast sets, resulting in superior performance on zero-shot testing, few-shot finetuning, and backward transferring in brain infarct and brain tumor segmentation tasks. Additionally, for self-supervised pretrain, the proposed method is able to maximize the pretrain data and facilitate transferring to diverse downstream tasks with variable sets of input modalities.

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