Social Inclusion of Smart Transportation:Case of Shanghai

Master of Science i global ledelse - Nord universitet 202

Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, the higher-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in clinical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network-efficiency and annotation-efficiency medical image segmentation. Specifically, our core Graph Flow Distillation transfer the essence of cross-layer variations from a well-trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is designed to purify the knowledge of the teacher network, which is also beneficial for the stabilization of training procedure. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. Extensive experiments conducted on Gastric Cancer Segmentation Dataset and Synapse Multi-organ Segmentation Dataset demonstrate the prominent ability of our method which achieves state-of-the-art performance on these different-modality and multi-category medical image datasets. Moreover, we demonstrate the effectiveness of our Graph Flow through a new semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow

Smoothing Spline ANOVA Models and their Applications in Complex and Massive Datasets

Complex and massive datasets can be easily accessed using the newly developed data acquisition technology. In spite of the fact that the smoothing spline ANOVA models have proven to be useful in a variety of fields, these datasets impose the challenges on the applications of the models. In this chapter, we present a selected review of the smoothing spline ANOVA models and highlight some challenges and opportunities in massive datasets. We review two approaches to significantly reduce the computational costs of fitting the model. One real case study is used to illustrate the performance of the reviewed methods

FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMAE for self-supervised pre-training for medical image segmentation. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the high-level semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our proposed FreMAE over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMAE could consistently bring considerable improvements to the model performance. To the best our knowledge, this is the first attempt towards MIM with Fourier Transform in medical image segmentation

The cosmic ray test of MRPCs for the BESIII ETOF upgrade

In order to improve the particle identification capability of the Beijing Spectrometer III (BESIII),t is proposed to upgrade the current endcap time-of-flight (ETOF) detector with multi-gap resistive plate chamber (MRPC) technology. Aiming at extending ETOF overall time resolution better than 100ps, the whole system including MRPC detectors, new-designed Front End Electronics (FEE), CLOCK module, fast control boards and time to digital modules (TDIG), was built up and operated online 3 months under the cosmic ray. The main purposes of cosmic ray test are checking the detectors' construction quality, testing the joint operation of all instruments and guaranteeing the performance of the system. The results imply MRPC time resolution better than 100$ps$, efficiency is about 98$\%$ and the noise rate of strip is lower than 1$Hz/$($scm^{2}$) at normal threshold range, the details are discussed and analyzed specifically in this paper. The test indicates that the whole ETOF system would work well and satisfy the requirements of upgrade