With intense global competition, many enterprises adopt platform strategies to develop diverse products with a lower corresponding increase in cost and a shorter lead time. In the past, enormous efforts have been made in investigating product platforms. Recent researches and practices show that developing Uncertainty-Oriented Product Platform (UOPP) is a promising approach to improving the adaptability of enterprises to meet uncertain markets, customer requirements, technologies, policies, and regulations, etc., which includes flexible product platform, adaptable product platform, market-driven product platform, and sustainable product platform. This paper provides a comprehensive review in this field, including the connotation of uncertainty, the concepts of emerging UOPPs, the development process of UOPP, as well as the development technologies in each step. It also highlights the future research framework, opportunities and challenges. This literature review lays the foundation for future research of product platform design under uncertainty.
The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.
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