Search

In recent years, the food delivery industry has had significant growth. In 2020, COVID-19 pushes this industry to be general for society. The food delivery industry helps customers’ lives be convenient. In China, Meituan is at the leading position in the food delivery market. Their business model has a huge effect on other food delivery companies. Fantuan is a food delivery company in Western countries, they usually work with local Chinese restaurants and deliver food to the customers. This paper analyzes both Meituan and Fantuan in macro, meso and micro way, to find out their status. This paper analyzes the governments’ politics, country economy, society and technology to find out how external factors affect companies’ business. This paper can depend on their competition mechanism to explain the reason for Meituan being the top food delivery company in China. Plus, this paper provides advice for Fantuan to improve their business in the Future.

Background and Aim Methods Results Conclusion There are no previous studies in which computer‐aided diagnosis (CAD) diagnosed colorectal cancer (CRC) subtypes correctly. In this study, we developed an original CAD for the diagnosis of CRC subtypes.Pretraining for the CAD based on ResNet was performed using ImageNet and five open histopathological pretraining image datasets (HiPreD) containing 3 million images. In addition, sparse attention was introduced to improve the CAD compared to other attention networks. One thousand and seventy‐two histopathological images from 29 early CRC cases at Kyoto Prefectural University of Medicine from 2019 to 2022 were collected (857 images for training and validation, 215 images for test). All images were annotated by a qualified histopathologist for segmentation of normal mucosa, adenoma, pure well‐differentiated adenocarcinoma (PWDA), and moderately/poorly differentiated adenocarcinoma (MPDA). Diagnostic ability including dice sufficient coefficient (DSC) and diagnostic accuracy were evaluated.Our original CAD, named Colon‐seg, with the pretraining of both HiPreD and ImageNET showed a better DSC (88.4%) compared to CAD without both pretraining (76.8%). Regarding the attentional mechanism, Colon‐seg with sparse attention showed a better DSC (88.4%) compared to other attentional mechanisms (dual: 79.7%, ECA: 80.7%, shuffle: 84.7%, SK: 86.9%). In addition, the DSC of Colon‐seg (88.4%) was better than other types of CADs (TransUNet: 84.7%, MultiResUnet: 86.1%, Unet++: 86.7%). The diagnostic accuracy of Colon‐seg for each histopathological type was 94.3% for adenoma, 91.8% for PWDA, and 92.8% for MPDA.A deep learning‐based CAD for CRC subtype differentiation was developed with pretraining and fine‐tuning of abundant histopathological images. [ABSTRACT FROM AUTHOR]