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.

翻译：内镜超声引导下细针穿刺抽吸术（EUS-FNA）用于检测胰腺癌。EUS-FNA采用内镜超声将细针插入肿瘤，获取胰腺组织碎片。随后对所采集的胰腺组织碎片进行染色，以分类其是否为胰腺癌。然而，染色和视觉检查耗时较长。此外，若染色后无法对胰腺组织碎片进行检测，则需择日重新取样。因此，本研究旨在从未经染色的图像中判断样本是否适用于检查，并超越专科医师的视觉分类准确率。染色前的图像分类可减少染色所需时间及患者负担。然而，本研究所用的胰腺组织碎片图像中，胰腺组织仅占图像局部区域，因此无法通过对整张图像进行处理来成功分类。为此，我们提出一种基于MetaFormer框架的变形Transformer（DeformableFormer），该架构由Vision Transformer的广义模型构成，并在TokenMixer部分引入变形卷积。与现有方法相比，所提出的DeformableFormer可通过变形卷积更局部化、更动态地提取特征，从而实现对目标的针对性特征提取。为评估该方法，我们对两类胰腺组织碎片（适用于检测与不适用于检测）进行分类。实验结果表明，本方法的准确率优于专科医师及传统方法。