pinecone/core-2020-05-10-deduplication huggingface dataset:
Deep Retinal Image Understanding

This paper presents Deep Retinal Image Understanding (DRIU), a unified
framework of retinal image analysis that provides both retinal vessel and optic
disc segmentation. We make use of deep Convolutional Neural Networks (CNNs),
which have proven revolutionary in other fields of computer vision such as
object detection and image classification, and we bring their power to the
study of eye fundus images. DRIU uses a base network architecture on which two
set of specialized layers are trained to solve both the retinal vessel and
optic disc segmentation. We present experimental validation, both qualitative
and quantitative, in four public datasets for these tasks. In all of them, DRIU
presents super-human performance, that is, it shows results more consistent
with a gold standard than a second human annotator used as control.Comment: MICCAI 2016 Camera Read
Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69 % and 80 %, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully
  Convolutional Neural Networks and 3D Conditional Random Fields

Automatic segmentation of the liver and its lesion is an important step
towards deriving quantitative biomarkers for accurate clinical diagnosis and
computer-aided decision support systems. This paper presents a method to
automatically segment liver and lesions in CT abdomen images using cascaded
fully convolutional neural networks (CFCNs) and dense 3D conditional random
fields (CRFs). We train and cascade two FCNs for a combined segmentation of the
liver and its lesions. In the first step, we train a FCN to segment the liver
as ROI input for a second FCN. The second FCN solely segments lesions from the
predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a
dense 3D CRF that accounts for both spatial coherence and appearance. CFCN
models were trained in a 2-fold cross-validation on the abdominal CT dataset
3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based
semantic liver and lesion segmentation achieves Dice scores over 94% for liver
with computation times below 100s per volume. We experimentally demonstrate the
robustness of the proposed method as a decision support system with a high
accuracy and speed for usage in daily clinical routine.Comment: Accepted at MICCAI 2016. Source code available on
  https://github.com/IBBM/Cascaded-FC
Deep Learning for Multi-Task Medical Image Segmentation in Multiple
  Modalities

Automatic segmentation of medical images is an important task for many
clinical applications. In practice, a wide range of anatomical structures are
visualised using different imaging modalities. In this paper, we investigate
whether a single convolutional neural network (CNN) can be trained to perform
different segmentation tasks.
  A single CNN is trained to segment six tissues in MR brain images, the
pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA.
The CNN therefore learns to identify the imaging modality, the visualised
anatomical structures, and the tissue classes.
  For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this
combined training procedure resulted in a segmentation performance equivalent
to that of a CNN trained specifically for that task, demonstrating the high
capacity of CNN architectures. Hence, a single system could be used in clinical
practice to automatically perform diverse segmentation tasks without
task-specific training
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to
treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming
the preferred choice of treatment due to its minimally invasive nature.
However, due to the limited field of view of the endoscope, surgeons rely on
navigation systems to guide them within the nasal cavity. State of the art
navigation systems report registration accuracy of over 1mm, which is large
compared to the size of the nasal airways. We present an anatomically
constrained video-CT registration algorithm that incorporates multiple video
features. Our algorithm is robust in the presence of outliers. We also test our
algorithm on simulated and in-vivo data, and test its accuracy against
degrading initializations.Comment: 8 pages, 4 figures, MICCA
The Minimum Cost Connected Subgraph Problem in Medical Image Analysis

Several important tasks in medical image analysis can be stated in the form
of an optimization problem whose feasible solutions are connected subgraphs.
Examples include the reconstruction of neural or vascular structures under
connectedness constraints. We discuss the minimum cost connected subgraph
(MCCS) problem and its approximations from the perspective of medical
applications. We propose a) objective-dependent constraints and b) novel
constraint generation schemes to solve this optimization problem exactly by
means of a branch-and-cut algorithm. These are shown to improve scalability and
allow us to solve instances of two medical benchmark datasets to optimality for
the first time. This enables us to perform a quantitative comparison between
exact and approximative algorithms, where we identify the geodesic tree
algorithm as an excellent alternative to exact inference on the examined
datasets.Comment: accepted at MICCAI 201
3D Imaging from Video and Planar Radiography

International audienceIn this paper we consider dense volumetric modeling of moving samples such as body parts.Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples.We propose a novel method that uses a surface motion capture system associated to a single low-cost/low-dose planar X-ray imaging device for dense in-depth attenuation information.Our key contribution is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. The approach enables multiple sources of noise to be considered and takes advantage of limited prior information to solve an otherwise ill-posed problem.Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on simulated and in-vivo data
A Bayesian Model to Assess T2 Values and Their Changes Over Time in Quantitative MRI

International audienceQuantifying T2 and T2* relaxation times from MRI becomes a standard tool to assess modifications of biological tissues over time or differences between populations. However, due to the relationship between the relaxation time and the associated MR signals such an analysis is subject to error. In this work, we provide a Bayesian analysis of this relationship. More specifically, we build posterior distributions relating the raw (spin or gradient echo) acquisitions and the relaxation time and its modifications over acquisitions. Such an analysis has three main merits. First, it allows to build hierarchical models including prior information and regularisations over voxels. Second, it provides many estimators of the parameters distribution including the mean and the α-credible intervals. Finally, as credible intervals are available, testing properly whether the relaxation time (or its modification) lies within a certain range with a given credible level is simple. We show the interest of this approach on synthetic datasets and on two real applications in multiple sclerosis
Deriving Inverse Operators for Modal Logic

International audienceSpatial constraint systems are algebraic structures from concurrent constraint programming to specify spatial and epistemic behavior in multi-agent systems. We shall use spatial constraint systems to give an abstract characterization of the notion of normality in modal logic and to derive right inverse/reverse operators for modal languages. In particular, we shall identify the weakest condition for the existence of right inverses and show that the abstract notion of normality corresponds to the preservation of finite suprema. We shall apply our results to existing modal languages such as the weakest normal modal logic, Hennessy-Milner logic, and linear-time temporal logic. We shall discuss our results in the context of modal concepts such as bisimilarity and inconsistency invariance
Unifying heterogeneous state-spaces with lenses

Most verification approaches embed a model of program state into their semantic treatment. Though a variety of heterogeneous state-space models exists, they all possess common theoretical properties one would like to abstractly capture, such as the laws of programming. In this paper, we propose lenses as a universal state-space modelling solution. Lenses provide an abstract interface for manipulating data types through spatially-separated views. We define a lens algebra that allow their composition and comparison. We show how this algebra can be used to model variables and alphabets in Hoare and He’s Unifying Theories of Programming. The combination of lenses and relational algebra gives rise to a model for UTP in which its fundamental laws can be verified. Moreover, we illustrate how they can be used to model more state complex notions such as memory stores and parallel states. We provide a mechanisation in Isabelle/HOL
Hybrid automata as coalgebras

Publicado em "Theoretical aspects of computing - ICTAC 2016: 13th International Colloquium, Taipei, Taiwan, ROC, October 24–31, 2016, Proceedings". ISBN 978-3-319-46749-8Able to simultaneously encode discrete transitions and continuous
behaviour, hybrid automata are the de facto framework for the
formal specification and analysis of hybrid systems. The current paper
revisits hybrid automata from a coalgebraic point of view. This allows to
interpret them as state-based components, and provides a uniform theory
to address variability in their definition, as well as the corresponding
notions of behaviour, bisimulation, and observational semantics.FCT grants SFRH/BD/52234/2013, SFRH/BSAB/ 113890/2015ERDF - European Regional Development Fund, through the COMPETE Programme, and by National Funds through FCT within project PTDC/EEI-CTP/4836/201
Dynamic logic with binders and its application to the development of reactive systems

Publicado em "Theoretical aspects of computing - ICTAC 2016: 13th International Colloquium, Taipei, Taiwan, ROC, October 24–31, 2016, Proceedings". ISBN 978-3-319-46749-8This paper introduces a logic to support the specification and
development of reactive systems on various levels of abstraction, from
property specifications, concerning e.g. safety and liveness requirements,
to constructive specifications representing concrete processes. This is
achieved by combining binders of hybrid logic with regular modalities
of dynamic logics in the same formalism, which we call D↓-logic. The
semantics of our logic focuses on effective processes and is therefore given
in terms of reachable transition systems with initial states. The second
part of the paper resorts to this logic to frame stepwise development of
reactive systems within the software development methodology proposed
by Sannella and Tarlecki. In particular, we instantiate the generic concepts
of constructor and abstractor implementations by using standard
operators on reactive components, like relabelling and parallel composition,
as constructors, and bisimulation for abstraction. We also study
vertical composition of implementations which relies on the preservation
of bisimularity by the constructions on labeleld transition systems.FCT individual grants SFRH/BPD/103004/2014 and SFRH/BSAB/113890/2015ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Cência e a Tecnologia within project POCI-01-0145-FEDER-016692 and UID/MAT/04106/2013 at CIDM
DataChopin - Designing interactions for visualisation composition in a co-located, cooperative environment

This article presents our interaction design for <i>DataChopin</i>, based on an extensive survey and classification of visualisation software for exploratory data analysis. Its distinctive characteristics are the use of a large-scale display wall as a shared desktop, as well as flexible composition mechanisms for incremental and piece-wise construction of visualisations. We chose composability as a guiding principle in our design, since it is essential to open-ended exploration, as well as collaborative analysis. For one, it enables truly exploratory inquiry by letting users freely examine different combinations of data, rather than offering a predetermined set of choices. Perhaps more importantly, it provides a foundation for data analysis through collaborative interaction with visualisations. If data and visualisations are composable, they can split into independent parts and recombined during the analytical process, allowing analysts to seamlessly transition between closely- and loosely-coupled work
Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam

Urban growth has been widely studied and many models (in particular Cellular Automata and Agent-Based Models) have been developed. Most of these models rely on two representations of the geographic space: raster and vector. Both representations have their own strengths and drawbacks. The raster models are simpler to implement and require less data, which explains their success and why most of urban growth models are based on this representation. However, they are not adapted to microscopic dynamics such as, for example, the construction of buildings. To reach such goal, a vector-based representation of space is mandatory. However, very few vector models exist, and none of them is easily adaptable to different case studies. In this paper, we propose to use a simple raster model and to adapt it to a vector representation of the geographic space and processes allowing studying urban growth at fine scale. Both models have been validated by a case study concerning the city of Can Tho, Vietnam
Towards User-Aware Service Composition

Our everyday life is more and more supported by the information technology in general and specific services provided by means of our electronic devices. The AMBIT project (Algorithms and Models for Building context-dependent Information delivery Tools) aims at providing a support to develop services that are automatically tailored based on the user profile. However, while the adaptation of the single services is the first step, the next step is to achieve adaptation in the composition of different services. In this paper, we explore how services can be composed in a user-aware way, in order to decide the composition that better meets users’ requirements. That is, we exploit the user profile not only to provide her customized services, but also to compose them in a suitable way
CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform. In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models. The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach
Fast Bilateral Symmetry Detection Using Inverted Gradient Hash Maps

This paper presents a fast and novel algorithm for bilateral symmetry detection based on inverted gradient hash maps (IGHMs). A hash map is an associative array that stores image gradient magnitudes and orientations in the form of an inverted index. This mapping of image gradients to their locations permits points of interest to be located very rapidly without needing to search through the image. Unlike many symmetry operators it is able to detect large-scale symmetry. The method is described and experimentally evaluated against existing methods for bilateral symmetry detection.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Scalable and Fast Top-k Most Similar Trajectories Search Using MapReduce In-Memory

Top-k most similar trajectories search (k-NN) is frequently used as classification algorithm and recommendation systems in spatial-temporal trajectory databases. However, k-NN trajectories is a complex operation, and a multi-user application should be able to process multiple k-NN trajectories search concurrently in large-scale data in an efficient manner. The k-NN trajectories problem has received plenty of attention, however, state-of-the-art works neither consider in-memory parallel processing of k-NN trajectories nor concurrent queries in distributed environments, or consider parallelization of k-NN search for simpler spatial objects (i.e. 2D points) using MapReduce, but ignore the temporal dimension of spatial-temporal trajectories. In this work we propose a distributed parallel approach for k-NN trajectories search in a multi-user environment using MapReduce in-memory. We propose a space/time data partitioning based on Voronoi diagrams and time pages, named Voronoi Pages, in order to provide both spatial-temporal data organization and process decentralization. In addition, we propose a spatial-temporal index for our partitions to efficiently prune the search space, improve system throughput and scalability. We implemented our solution on top of Spark’s RDD data structure, which provides a thread-safe environment for concurrent MapReduce tasks in main-memory. We perform extensive experiments to demonstrate the performance and scalability of our approach.Full Tex
Virtual samples construction using image-block-stretching for face recognition

© Springer International Publishing AG 2016.Face recognition encounters the problem that multiple samples of the same object may be very different owing to the deformation of appearances. To synthesizing reasonable virtual samples is a good way to solve it. In this paper, we introduce the idea of image-block-stretching to generate virtual images for deformable faces. It allows the neighbored image blocks to be stretching randomly to reflect possible variations of the appearance of faces. We demonstrate that virtual images obtained using image-block-stretching and original images are complementary in representing faces. Extensive classification experiments on face databases show that the proposed virtual image scheme is very competent and can be combined with a number of classifiers, such as the sparse representation classification, to achieve surprising accuracy improvement
Flexible distribution of existing Web interfaces: an architecture involving developers and end-users

This paper presents a novel approach towards the opportunistic and lightweight distribution of existent Web User Interfaces. We describe an architecture that allows end-users to collect UI objects into a distributed UI-Component-oriented PIM, accessible from different user’s devices. Once in the PIM, the collected UI components are wrapped with different DUI-based behaviours that may be triggered by the user, as PIM’s objects plug-ins. We present an overview of the architecture; some default DUI-based behaviours are introduced and illustrated through examples. Besides, we show how the architecture supports the development of new DUI-based behaviours
Early Experiences with Crowdsourcing Airway Annotations in Chest CT

Measuring airways in chest computed tomography (CT) images is important for
characterizing diseases such as cystic fibrosis, yet very time-consuming to
perform manually. Machine learning algorithms offer an alternative, but need
large sets of annotated data to perform well. We investigate whether
crowdsourcing can be used to gather airway annotations which can serve directly
for measuring the airways, or as training data for the algorithms. We generate
image slices at known locations of airways and request untrained crowd workers
to outline the airway lumen and airway wall. Our results show that the workers
are able to interpret the images, but that the instructions are too complex,
leading to many unusable annotations. After excluding unusable annotations,
quantitative results show medium to high correlations with expert measurements
of the airways. Based on this positive experience, we describe a number of
further research directions and provide insight into the challenges of
crowdsourcing in medical images from the perspective of first-time users
PAMOP project: computations in support of experiments and astrophysical
  applications

Our computation effort is primarily concentrated on support of current and
future measurements being carried out at various synchrotron radiation
facilities around the globe, and photodissociation computations for
astrophysical applications. In our work we solve the Schr\"odinger or Dirac
equation for the appropriate collision problem using the R-matrix or R-matrix
with pseudo-states approach from first principles. The time dependent
close-coupling (TDCC) method is also used in our work. A brief summary of the
methodology and ongoing developments implemented in the R-matrix suite of
Breit-Pauli and Dirac-Atomic R-matrix codes (DARC) is presented.Comment: 17 pages, 10 figures: chapter in the book, High Performance Computing
  in Science and Engineering'16, edited by W. E. Nagel, D. B. Kr\"oner, and M.
  Reich (Springer, New York and Berlin, 2017