Automatic Brain Tumor Segmentation by Deep Convolutional Networks and Graph Cuts

Brain tumor segmentation in magnetic resonance imaging (MRI) is helpful for diagnostics, growth rate prediction, tumor volume measurements and treatment planning of brain tumor. The difficulties for brain tumor segmentation are mainly due to high variation of brain tumors in size, shape, regularity, location, and their heterogeneous appearance (e.g., contrast, intensity and texture variation for different tumors). Due to recent advances in deep convolutional neural networks for semantic image segmentation, automatic brain tumor segmentation is a promising research direction. This thesis investigates automatic brain tumor segmentation by combining deep convolutional neural network with regularization by a graph cut. We investigate several deep convolutional network structures that have been successful in semantic and medical image segmentation. Since the tumor pixels account for a very small portion in the whole brain slice, segmenting the tumor from the background is a highly imbalanced dense prediction task. We use a loss function that takes the imbalance of the training data into consideration. In the second part of the thesis, we improve the segmentation results of a deep neural network by using optimization framework with graph cuts. The graph cut framework can improve segmentation boundaries by making them more smooth and regular. The main issue when using the segmentation results of convolutional neural networks for the graph cut optimization framework is to convert tumor probabilities learned by a convolutional network into data terms. We investigate several possible ways that take into consideration the segmentation artifacts by convolutional neural networks. In experiments, we present the segmentation results by different deep convolutional neural network structures, e.g., fully convolutional neural network, dilated residual network and UNet. Also, we compare the combination of U-Net with different data terms for graph cut regularization to improve the neural network segmentation results. Experimental results show that the U-Net performs best with the intersection over union (IoU) for tumors of 0.7286. The IoU for tumors is improved to 0.7530 by training on three slices. Also, the IoU for tumors is improved to 0.7713 by U-Net with balanced loss function. The IoU for tumors is further improved to 0.8078 by graph cut regularization

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.Comment: Accepted at ACL202

Top Management Team Heterogeneity Influence on Technological Innovation - The Empirical Analysis on the IT Enterprises from Six

In today’s increasingly complex economic environment, the developments of enterprises rely more and more on the strength of senior management team. Especially for the IT companies, who rely on the high-tech development. This article analyzes the influence of the heterogeneity in TMT’s education background, age, and working years on technological innovation, depending on the data collected from IT industry from six central provinces. The empirical research shows that the heterogeneity of term and degree will have positive effect on the enterprise technological innovation, while the heterogeneity of age has no significant influence on technological innovation. The IT enterprises from six provinces should absorb the talents who have different education background, accept the new members and make full use of their intelligence to improve the enterprises technological innovation ability and promote competitiveness

Executives Political Connection and Over-investment in New Energy Companies：Empirical Evidence from China\u27s Capital Market

Establishing links between business and government is a common phenomenon in the world. Using data of new energy companies listed in Shenzhen and Shanghai Stock Exchange，the paper examines the relationship between political connection and firms’ over-investment. We find that executives political connection is a significant promotion of firms’ over-investment; the political connection is divided into the central- and local-level, and further tests find that political connections with different levels have no significant impact on firms’ over-investment. Our findings provide an empirical evidence for strengthening the Governance Reform of the government

Little flock - designing and evaluating a mobile application for Christians in house groups

Mobile devices have been bringing changes and convenience to people both in their social and personal lives. Due to the unique features resulting from their mobility and constrained capacity, designing for mobile interfaces has been gaining an increasing interest and discussion in the field of Human Computer Interaction. This paper presents the design and evaluation process of an app to be used in Christian House Groups, following a user-centred approach. The project began with six exploratory interviews, of which the results were used to inform the first low-fidelity prototype on paper. Five users were then invited to evaluate the prototype and hence a refined design was produced in the form of an Interactive Wireframe. A final Highfidelity Prototype was produced after evaluating with another six end users

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning

Forgetting refers to the loss or deterioration of previously acquired information or knowledge. While the existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new tasks, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we aim to present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, in future work, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}