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Endoscopic Ultrasound-Fine Needle Aspiration (EUS-FNA) is used to examinepancreatic cancer. EUS-FNA is an examination using EUS to insert a thin needleinto the tumor and collect pancreatic tissue fragments. Then collectedpancreatic tissue fragments are then stained to classify whether they arepancreatic cancer. However, staining and visual inspection are time consuming.In addition, if the pancreatic tissue fragment cannot be examined afterstaining, the collection must be done again on the other day. Therefore, ourpurpose is to classify from an unstained image whether it is available forexamination or not, and to exceed the accuracy of visual classification byspecialist physicians. Image classification before staining can reduce the timerequired for staining and the burden of patients. However, the images ofpancreatic tissue fragments used in this study cannot be successfullyclassified by processing the entire image because the pancreatic tissuefragments are only a part of the image. Therefore, we propose aDeformableFormer that uses Deformable Convolution in MetaFormer framework. Thearchitecture consists of a generalized model of the Vision Transformer, and weuse Deformable Convolution in the TokenMixer part. In contrast to existingapproaches, our proposed DeformableFormer is possible to perform featureextraction 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 tissuefragments; available and unavailable for examination. We demonstrated that ourmethod outperformed the accuracy by specialist physicians and conventionalmethods.

DeformableFormer: Classification of Endoscopic Ultrasound Guided Fine Needle Biopsy in Pancreatic Diseases