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

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

Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout

Designing an automatic checkout system for retail stores at the human level accuracy is challenging due to similar appearance products and their various poses. This paper addresses the problem by proposing a method with a two-stage pipeline. The first stage detects class-agnostic items, and the second one is dedicated to classify product categories. We also track the objects across video frames to avoid duplicated counting. One major challenge is the domain gap because the models are trained on synthetic data but tested on the real images. To reduce the error gap, we adopt domain generalization methods for the first-stage detector. In addition, model ensemble is used to enhance the robustness of the 2nd-stage classifier. The method is evaluated on the AI City challenge 2022 -- Track 4 and gets the F1 score $40\%$ on the test A set. Code is released at the link https://github.com/cybercore-co-ltd/aicity22-track4

Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks

Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation. They provide state-of-the-art performance compared to classical image processing algorithms. However, the major downside of these methods is the high computational complexity, reliance on high-performance hardware like GPUs and the inherent black-box nature of the model. In this paper, we propose quantised stand-alone self-attention based models as an alternative to traditional CNNs. In the proposed class of networks, convolutional layers are replaced with stand-alone self-attention layers, and the network parameters are quantised after training. We experimentally validate the performance of our method on classification and segmentation tasks. We observe a $50-80\%$ reduction in model size, $60-80\%$ lesser number of parameters, $40-85\%$ fewer FLOPs and $65-80\%$ more energy efficiency during inference on CPUs. The code will be available at \href {https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}{https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}.Comment: Accepted at MICCAI 2022 FAIR Worksho