Using Feature Extraction From Deep Convolutional Neural Networks for Pathological Image Analysis and Its Visual Interpretability

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the multi-view scheme was designed in the proposed system that performs lesion detection for region proposal at higher magnification first and then conducts lesion classification at lower magnification for each detected lesion. The majority voting scheme was adopted to improve the error tolerance of the system in lesion-wise prediction. For all collected 186 slides, the slide-wise prediction accuracy rate strikes to 95.16% (177/186) in binary classification to predict carcinoma (malignant) or non-carcinoma (benign), and the sensitivity for cases with carcinoma reaches 96.36% (106/110). In multi-classification, the accuracy rate is 92.47% (172/186) when predicting Non-Carcinoma, DCIS, and IDC for each slide. Most importantly, the interpretability for the mechanism of the proposed CAD system is provided from the pathological perspective. The experimental results show that the morphological characteristics and co-occurrence properties learned by the deep learning models for lesion detection and classification meet the clinical rules in diagnosis. Accordingly, the pathological interpretability of the deep features not only enhances the reliability of the proposed CAD system to gain acceptance from medical specialists, but also facilitates the development of deep learning frameworks for various tasks in pathology

The Relationship between Stock Price and EPS: Evidence Based on Taiwan Panel Data

In this study, we use panel cointegration methods to investigate the relationship between stock prices and earnings-per-share (EPS). Furthermore, we consider whether stock prices respond to EPS under the different level of growth rate of operating revenue. The empirical result indicated that the cointegration relationship existed between stock prices and EPS in the long-run. Furthermore, we found that for the firm with a high level of growth rate, EPS has less power in explaining the stock prices however, for the firm with a low level of growth rate, EPS has a strong impact in stock prices.Earnings Response Coefficient (ERC)

Complete Polarization Control in Multimode Fibers with Polarization and Mode Coupling

Multimode optical fibers have seen increasing applications in communication, imaging, high-power lasers and amplifiers. However, inherent imperfections and environmental perturbations cause random polarization and mode mixing, making the output polarization states very different from the input one. This poses a serious issue for employing polarization sensitive techniques to control light-matter interactions or nonlinear optical processes at the distal end of a fiber probe. Here we demonstrate a complete control of polarization states for all output channels by only manipulating the spatial wavefront of a laser beam into the fiber. Arbitrary polarization states for individual output channels are generated by wavefront shaping without constraint on input polarizations. The strong coupling between spatial and polarization degrees of freedom in a multimode fiber enables full polarization control with spatial degrees of freedom alone, transforming a multimode fiber to a highly-efficient reconfigurable matrix of waveplates

Performance-based Fire Safety Design for Existing Small-scale Hospitals

AbstractThe new era of National Health Insurance in 2000 has had a significant impacted on the management and operation of smallscale hospitals. In response to social needs, and in order to survive under the new insurance system, some small-scale hospitals have transformed or established new Respiratory Care Wards by using existing hospital space. According to the 2009 statistics released by Department of Health, Executive Yuan, there are a total of 307 small-scale medical institutes which provide servicesunder 99 beds. Compared with other large-scale medical centers and general hospitals, small-scale hospitals cannot properly deal with safety management and response to emergency evacuation due to lack of facilities, equipment and human resources. Therefore, small-scale hospitals face a major challenge in emergency response once a fire has occurred. As a result of such a situation, this study has focused mainly on Respiratory Care Wards (RCW) where patients are unable to evacuate. It hopes to analyse the safety management, and emergency response in small-scale hospitals by means of understanding the space characteristics and fire risk. Through on-site surveys, we can understand the fire risk, space features, patient characteristics, facilities and equipment. With reference to the related regulations of hospital emergency management and response, we will propose some fire safety engineering approaches, such as refuge areas in horizontal evacuation and so-called “besieged zones” for “defense-in-place”, etc., to provide some alternative measures to improve fire safety for those small-scale hospitals

Effects of System Characteristics on Adopting Web-Based Advanced Traveller Information System: Evidence from Taiwan

This study proposes a behavioural intention model that integrates information quality, response time, and system accessibility into the original technology acceptance model (TAM) to investigate whether system characteristics affect the adoption of Web-based advanced traveller information systems (ATIS). This study empirically tests the proposed model using data collected from an online survey of Web-based advanced traveller information system users. Con­firmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and structural equation modelling (SEM) was used to evaluate the structural model. The results indicate that three system characteristics had indirect effects on the intention to use through perceived usefulness, perceived ease of use, and attitude toward using. Information quality was the most im­portant system characteristic factor, followed by response time and system accessibility. This study presents implica­tions for practitioners and researchers, and suggests direc­tions for future research.</p