  The manifestation of symptoms associated with lung diseases can vary in
different depths for individual patients, highlighting the significance of 3D
information in CT scans for medical image classification. While Vision
Transformer has shown superior performance over convolutional neural networks
in image classification tasks, their effectiveness is often demonstrated on
sufficiently large 2D datasets and they easily encounter overfitting issues on
small medical image datasets. To address this limitation, we propose a
Diffusion-based 3D Vision Transformer (Diff3Dformer), which utilizes the latent
space of the Diffusion model to form the slice sequence for 3D analysis and
incorporates clustering attention into ViT to aggregate repetitive information
within 3D CT scans, thereby harnessing the power of the advanced transformer in
3D classification tasks on small datasets. Our method exhibits improved
performance on two different scales of small datasets of 3D lung CT scans,
surpassing the state of the art 3D methods and other transformer-based
approaches that emerged during the COVID-19 pandemic, demonstrating its robust
and superior performance across different scales of data. Experimental results
underscore the superiority of our proposed method, indicating its potential for
enhancing medical image classification tasks in real-world scenarios.
