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The rapid on-site evaluation (ROSE) technique can signifi-cantly acceleratethe diagnosis of pancreatic cancer by im-mediately analyzing the fast-stainedcytopathological images. Computer-aided diagnosis (CAD) can potentially addressthe shortage of pathologists in ROSE. However, the cancerous patterns varysignificantly between different samples, making the CAD task extremelychallenging. Besides, the ROSE images have complicated perturbations regardingcolor distribution, brightness, and contrast due to different stainingqualities and various acquisition device types. To address these challenges, weproposed a shuffle instances-based Vision Transformer (SI-ViT) approach, whichcan reduce the perturbations and enhance the modeling among the instances. Withthe regrouped bags of shuffle instances and their bag-level soft labels, theapproach utilizes a regression head to make the model focus on the cells ratherthan various perturbations. Simultaneously, combined with a classificationhead, the model can effectively identify the general distributive patternsamong different instances. The results demonstrate significant improvements inthe classification accuracy with more accurate attention regions, indicatingthat the diverse patterns of ROSE images are effectively extracted, and thecomplicated perturbations are significantly reduced. It also suggests that theSI-ViT has excellent potential in analyzing cytopathological images. The codeand experimental results are available at https://github.com/sagizty/MIL-SI.

Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification