  Endoscopic Ultrasound-Fine Needle Aspiration (EUS-FNA) is used to examine
pancreatic cancer. EUS-FNA is an examination using EUS to insert a thin needle
into the tumor and collect pancreatic tissue fragments. Then collected
pancreatic tissue fragments are then stained to classify whether they are
pancreatic cancer. However, staining and visual inspection are time consuming.
In addition, if the pancreatic tissue fragment cannot be examined after
staining, the collection must be done again on the other day. Therefore, our
purpose is to classify from an unstained image whether it is available for
examination or not, and to exceed the accuracy of visual classification by
specialist physicians. Image classification before staining can reduce the time
required for staining and the burden of patients. However, the images of
pancreatic tissue fragments used in this study cannot be successfully
classified by processing the entire image because the pancreatic tissue
fragments are only a part of the image. Therefore, we propose a
DeformableFormer that uses Deformable Convolution in MetaFormer framework. The
architecture consists of a generalized model of the Vision Transformer, and we
use Deformable Convolution in the TokenMixer part. In contrast to existing
approaches, our proposed DeformableFormer is possible to perform feature
extraction more locally and dynamically by Deformable Convolution. Therefore,
it is possible to perform suitable feature extraction for classifying target.
To evaluate our method, we classify two categories of pancreatic tissue
fragments; available and unavailable for examination. We demonstrated that our
method outperformed the accuracy by specialist physicians and conventional
methods.
