Surgical Phase Recognition: From Public Datasets to Real-World Data

Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and longer videos, which may be missing in curated public datasets. In this work, we study (i) the possible data distribution discrepancy between videos observed in a given medical center and videos from existing public datasets, and (ii) the potential impact of this distribution difference on model development. To this end, we gathered a large, private dataset of 384 Lap Chole videos. Our dataset contained all videos, including emergency surgeries and teaching cases, recorded in a continuous time frame of five years. We observed strong differences between our dataset and the most commonly used public dataset for surgical phase recognition, Cholec80. For instance, our videos were much longer, included additional phases, and had more complex transitions between phases. We further trained and compared several state-of-the-art phase recognition models on our dataset. The models’ performances greatly varied across surgical phases and videos. In particular, our results highlighted the challenge of recognizing extremely under- represented phases (usually missing in public datasets); the major phases were recognized with at least 76 percent recall. Overall, our results highlighted the need to better understand the distribution of the video data phase that recognition models are trained on

Using covariates for improving the minimum redundancy maximum relevance feature selection method

Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets

The Transfer of Marketing Know-How By Multinational Companies: a Case Study in Turkey.

Economic development strategies of developing countries have concentrated on production, investment and finance, while the role of marketing in development has drawn little attention. There has been growing evidence that the lack of marketing know-how has been partly responsible for countriesPh.D.MarketingUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/157882/1/8025647.pd

Computerised graft monitoring.

Many vascular disorders require surgical procedures to overcome failing blood supply. Deficient arteries are replaced by prosthetic or vein bypass grafts to recover normal blood flow. However some grafts fail after operation. Therefore graft surveillance programs are important to increase the patency rate of grafts. Although there are a number of methods for medium and long term graft surveillance, these are not suitable for monitoring grafts immediately after operation to detect early graft failures which account for 20% of the total. This dissertation describes a computerised graft monitoring system which is suitable for continuous or intermittent monitoring of grafts immediately after surgery. The system comprises a floating point DSP board, an IBM compatible computer and a purpose built CW Doppler board. The Doppler board is designed to be installed in the computer. The possibility of implementation of DSP algorithms for obtaining directional information is extensively discussed. This study shows that digital techniques outperform their analogue counterparts. Therefore in this system, apart from the quadrature demodulation of the Doppler signals all processes are implemented digitally. Maximum frequency envelope detection algorithms are also discussed. The results obtained from monitoring seven patients are presented and practical difficulties encountered during the monitoring process are highlighted

Improving Medical Diagnosis Reliability Using Boosted C5.0 Decision Tree empowered by Particle Swarm Optimization

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods