Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

The UCF Report, Vol. 16 No. 17, March 18, 1994

Computer team places seventh in world: UCF finishes fourth in nation after solving four problems; Founders\u27 Day convocation set for April 6

Head-Worn Displays for NextGen

The operating concepts emerging under the Next Generation air transportation system (NextGen) require new technology and procedures - not only on the ground-side - but also on the flight deck. Flight deck display and decision support technologies are specifically targeted to overcome aircraft safety barriers that might otherwise constrain the full realization of NextGen. One such technology is the very lightweight, unobtrusive head-worn display (HWD). HWDs with an integrated head-tracking system are being researched as they offer significant potential benefit under emerging NextGen operational concepts. Two areas of benefit for NextGen are defined. First, the HWD may be designed to be equivalent to the Head-Up Display (HUD) using Virtual HUD concepts. As such, these operational credits may be provided to significantly more aircraft for which HUD installation is neither practical nor possible. Second, the HWD provides unique display capabilities, such as an unlimited field-of-regard. These capabilities may be integral to emerging NextGen operational concepts, eliminating safety issues which might otherwise constrain the full realization of NextGen. The paper details recent research results, current HWD technology limitations, and future technology development needed to realize HWDs as a enabling technology for NextGen

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care

The non-convex shape of (234) Barbara, the first Barbarian

Asteroid (234) Barbara is the prototype of a category of asteroids that has been shown to be extremely rich in refractory inclusions, the oldest material ever found in the Solar System. It exhibits several peculiar features, most notably its polarimetric behavior. In recent years other objects sharing the same property (collectively known as "Barbarians") have been discovered. Interferometric observations in the mid-infrared with the ESO VLTI suggested that (234) Barbara might have a bi-lobated shape or even a large companion satellite. We use a large set of 57 optical lightcurves acquired between 1979 and 2014, together with the timings of two stellar occultations in 2009, to determine the rotation period, spin-vector coordinates, and 3-D shape of (234) Barbara, using two different shape reconstruction algorithms. By using the lightcurves combined to the results obtained from stellar occultations, we are able to show that the shape of (234) Barbara exhibits large concave areas. Possible links of the shape to the polarimetric properties and the object evolution are discussed. We also show that VLTI data can be modeled without the presence of a satellite.Comment: 10 pages, 6 figure

Seasonal Movements, Migratory Behavior, and Site Fidelity of West Indian Manatees along the Atlantic Coast of the United States as Determined by Radio-telemetry

The study area encompassed the eastern coasts of Florida, Georgia, and South Carolina, including inland waterways such as the St. Johns River (Fig. 1). Manatees inhabited the relatively narrow band of water that lies between the barrier beaches and the mainland, occasionally venturing into the ocean close to shore. Between Miami and Fernandina Beach, Florida, 19 inlets provided manatees with corridors between the intracoastal waters and the Atlantic Ocean; the distance between adjacent inlets averaged 32 km(SD = 24 km) and varied from 3 to 88 km. Habitats used by manatees along this 900-km stretch ofcoastline varied widely and included estuaries, lagoons, rivers and creeks, shallow bays and sounds, and ocean inlets. Salinities in most areas were brackish, but ranged from completely fresh to completely marine. The predominant communities of aquatic vegetation also varied geographically and with salinity: seagrass meadows and mangrove swamps in brackish and marine waters along the southern half of peninsular Florida; salt marshes in northeastern Florida and Georgia; benthic macroalgae in estuarine and marine habitats; and a variety of submerged, floating, and emergent vegetation in freshwater rivers, canals, and streams throughout the region. Radio-telemetry has been used successfully to track manatees in other regions ofFlorida (Bengtson 1981, Powell and Rathbun 1984, Lefebvre and Frohlich 1986, Rathbun et al. 1990) and Georgia (Zoodsma 1991), but these early studies relied primarily on conventional VHF (very high frequency) transmitters and were limited in their spatial and temporal scope (see O'Shea and Kochman 1990 for overview). Typically, manatees were tagged at a thermal refuge in the winter and then tracked until the tag detached, usually sometime between the spring and fall of the same year. Our study differs from previous research on manatee movements in several important respects. First, we relied heavily on data from satellite-monitored transmitters using the Argos system, which yielded a substantially greater number of locations and more systematic collection of data compared to previous VHF tracking studies (Deutsch et al. 1998). Second, our tagging and tracking efforts encompassed the entire range of manatees along the Atlantic coast, from the Florida Keys to South Carolina, so inferences were not limited to a small geographic area. Third, we often used freshwater to lure manatees to capture sites, which allowed tagging in all months of the year; this provided more information about summer movement patterns than had previous studies which emphasized capture and tracking at winter aggregations. Finally, the study spanned a decade, and success in retagging animals and in replacing transmitters allowed long-term tracking ofmany individuals. This provided the opportunity to investigate variation in seasonal movements, migratory behavior, and site fidelity across years for individual manatees. (254 page document.

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde