  The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate
the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained
cytopathological images. Computer-aided diagnosis (CAD) can potentially address
the shortage of pathologists in ROSE. However, the cancerous patterns vary
significantly between different samples, making the CAD task extremely
challenging. Besides, the ROSE images have complicated perturbations regarding
color distribution, brightness, and contrast due to different staining
qualities and various acquisition device types. To address these challenges, we
proposed a shuffle instances-based Vision Transformer (SI-ViT) approach, which
can reduce the perturbations and enhance the modeling among the instances. With
the regrouped bags of shuffle instances and their bag-level soft labels, the
approach utilizes a regression head to make the model focus on the cells rather
than various perturbations. Simultaneously, combined with a classification
head, the model can effectively identify the general distributive patterns
among different instances. The results demonstrate significant improvements in
the classification accuracy with more accurate attention regions, indicating
that the diverse patterns of ROSE images are effectively extracted, and the
complicated perturbations are significantly reduced. It also suggests that the
SI-ViT has excellent potential in analyzing cytopathological images. The code
and experimental results are available at https://github.com/sagizty/MIL-SI.
