Convolutional Neural Network–Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study

Background: Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra- and interobserver variability, which affects diagnosis reliability. Objective: This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. Methods: In this study, we proposed a convolutional neural network (CNN) model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer. Results: Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for the whole classification task. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images. Conclusions: The proposed CNN architecture was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, and it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images

Combined Partial Motion Clips

We present a motion editing method for articulated figures using Combined Partial Motion Clips (CPMCs). CPMCs contain detailed motion information for some parts of the articulated figure. They can be used to edit base motions in such a way that the parts that are not defined in detail will still be affected thereby emulating the correlation that exists naturally between joint movements. This is achieved through the inclusion of equations in the CPMC that capture the effects of the detailed motion on other degrees of freedom of the articulated figure

A Novel In-Place Sorting Algorithm with O(n log z) Comparisons and O(n log z) Moves

Abstract—In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. In this paper, a novel in-place sorting algorithm is presented. The algorithm comprises two phases; rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O(1) auxiliary storage. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. Further, no auxiliary arithmetic operations with indices are required. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Experimental results also show that it outperforms other in-place sorting algorithms. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm. Keywords—Auxiliary storage sorting, in-place sorting, sorting. I

The role of Information Technology in Supporting Quality Teaching and Learning

This chapter investigates the use of information technology in support ing quality teaching and learning in university education in the Kingdom of Saudi Arabia. It is based on a comprehensive survey of academics that was undertaken in 2010 in seven Saudi universities. To the best of our knowledge, this re presents the only rigorous and comprehensive survey of this area yet undertaken in the Kingdom

Training Environment for Inferior Vena Caval Filter Placement

. This paper describes a Virtual Environment system designed to aid in training interventional radiologists in inferior vena cava filter placement. It is being developed as part of a VE simulator for a number of surgical and interventional radiology procedures at the Laboratory for Advanced Computer Applications in Medicine at the George Washington University. In this procedure a filter is placed in the inferior vena cava to prevent blood clots from the lower portion of the body from reaching the lungs and causing a pulmonary embolus. The simulation is designed to provide both tutorial and testing modes for the filter placement procedure. 1. Introduction Recently, Virtual Environment (VE) technology has been applied for simulating a variety of surgical and interventional radiology procedures. Such simulators can be used to allow the physician-in-training to master both conventional treatment protocols and also rarely encountered problem situations prior to his or her first encounter w..