References of "JODOGNE, Sébastien"
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See detailOrthanc - Logiciel libre pour l'imagerie médicale en milieu hospitalier
JODOGNE, Sébastien ULg; LENAERTS, Eric ULg; COUCKE, Philippe ULg et al

in Communications de la Conférence Francophone en Gestion et Ingénierie des Systèmes Hospitaliers (GISEH 2014) (2014, July)

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See detailUTILISATION DE L’IMAGERIE FONCTIONNELLE EN RADIOTHÉRAPIE
LALLEMAND, François ULg; LAKOSI, Ferenc ULg; HUSTINX, Roland ULg et al

in Revue Médicale de Liège (2014), 69(Supp 1), 20-28

Les progrès technologiques réalisés par l’image- rie médicale l’ont placée au centre de la prise en charge des patients oncologiques, tant au niveau du diagnostic, du pro - nostic et du suivi que dans la ... [more ▼]

Les progrès technologiques réalisés par l’image- rie médicale l’ont placée au centre de la prise en charge des patients oncologiques, tant au niveau du diagnostic, du pro - nostic et du suivi que dans la prise en charge thérapeutique. En effet, l’imagerie représente, à l’heure actuelle, la pierre angulaire des traitements de radiothérapie. Les objectifs du radiothérapeute sont d’irradier le plus précisément possible la tumeur à dose curative, tout en évitant les organes sains. Pour y arriver, le radiothérapeute utilise de façon routinière l’imagerie anatomique (Scanner et IRM). Depuis quelques années, le développement des différentes imageries métabo - liques et fonctionnelles, comme l’imagerie par émission de positons (PET-CT) et la résonnance magnétique fonctionnelle, ouvrent de nouvelles possibilités thérapeutiques grâce aux informations qu’elles apportent sur la biologie des tumeurs. Cet article décrit, de manière non exhaustive, les différentes imageries anatomiques et métaboliques à la disposition du radiothérapeute. [less ▲]

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See detailOrthanc - Lightweight, Scriptable DICOM Server for Medical Image Management in Radiotherapy
JODOGNE, Sébastien ULg; SOBCZAK, Sonia ULg; DEVILLERS, Magali ULg et al

in Radiotherapy & Oncology (2013), 106(2), 488

High-quality radiotherapy (RT) treatment planning requires the combination of information arising from multiple medical imaging modalities. For this reason, RT implies the setup and the management of ... [more ▼]

High-quality radiotherapy (RT) treatment planning requires the combination of information arising from multiple medical imaging modalities. For this reason, RT implies the setup and the management of complex flows of images between the various modalities and software of the hospital. Even though biomedical images are most commonly stored and transferred using the DICOM standard, it remains hard to automatize and optimize these clinical flows that are very specific to each hospital. This stems from the fact that programming the DICOM network protocol requires both a high level of familiarity with the DICOM standard as well as substantial experience in computer programming. This motivates the introduction of the Orthanc software in the medical practice to improve the RT imaging workflow. [less ▲]

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See detailOrthanc - A lightweight, RESTful DICOM server for healthcare and medical research
JODOGNE, Sébastien ULg; Bernard, Claire ULg; DEVILLERS, Magali ULg et al

in Proceedings, IEEE International Symposium on Biomedical Imaging: from Nano to Macro (2013, April)

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See detailAutomated platform for the quality control of PET-CT scanners following AFCN/FANC recommendations
Bernard, Claire ULg; Paulus, Cyril; JODOGNE, Sébastien ULg et al

in Proceedings of the Belgian Hospital Physicists Association Symposium (2013, February)

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See detailOrthanc - Lightweight, scriptable DICOM server for image management in radiotherapy departments
JODOGNE, Sébastien ULg; Sobczak, Sonia; DEVILLERS, Magali ULg et al

in Proceedings of the Belgian Hospital Physicists Association Symposium (2013, February)

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See detailOrthanc
JODOGNE, Sébastien ULg

Software (2012)

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See detailLearning Visual Representations for Perception-Action Systems
Piater, Justus ULg; JODOGNE, Sébastien ULg; Detry, Renaud ULg et al

in International Journal of Robotics Research (2011), 30(3), 294-307

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See detailP2P File Sharing for P2P Computing
Briquet, Cyril ULg; Dalem, Xavier ULg; Jodogne, Sébastien ULg et al

in Multiagent and Grid Systems (2009), 2009/5(2), 137-164

The transfer of large input data files in P2P computing Grids often leads to delays in Task completion times. Existing research related to this topic has been focused on the spatial grouping of Tasks, i.e ... [more ▼]

The transfer of large input data files in P2P computing Grids often leads to delays in Task completion times. Existing research related to this topic has been focused on the spatial grouping of Tasks, i.e. reuse of available data through data caching and data-aware scheduling. However, it tends to decrease the level of parallelism of Task execution. In this paper, this issue is addressed by integrating the BitTorrent P2P file sharing protocol, a novel Task selection scheduling algorithm, an existing online, data-aware Resource selection algorithm (similar to Storage Affinity), and caching support. These algorithms have been implemented in the Lightweight Bartering Grid middleware. The Java implementation relies exclusively on Free and Open Source data transfer software (Azureus, Apache FTP server, edtFTPj). The proposed data transfer architecture does not need Predictive Communications Ordering or an explicit deployment of an overlay network. It is also easily deployable. Our main contribution is the joint use of P2P computing and P2P file sharing technologies, enabling a highly scalable and adaptive data transfer architecture to support P2P computing. [less ▲]

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See detailLearning Visual Representations for Interactive Systems
Piater, Justus ULg; Jodogne, Sébastien ULg; Detry, Renaud ULg et al

in 14th International Symposium on Robotics Research (2009)

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See detailScheduling Data-Intensive Bags of Tasks in P2P Grids with BitTorrent-enabled Data Distribution
Briquet, Cyril ULg; Dalem, Xavier ULg; Jodogne, Sébastien ULg et al

in Fortino, Giancarlo; Mastroianni, Carlo; Pierre, Guillaume (Eds.) Proceedings of the Second Workshop on Use of P2P, GRID and Agents for the Development of Content Networks (2007, June 26)

Scheduling Data-Intensive Bags of Tasks in P2P Grids leads to transfers of large input data files, which cause delays in completion times. We propose to combine several existing technologies and patterns ... [more ▼]

Scheduling Data-Intensive Bags of Tasks in P2P Grids leads to transfers of large input data files, which cause delays in completion times. We propose to combine several existing technologies and patterns to perform efficient data-aware scheduling: (1) use of the BitTorrent P2P file sharing protocol to transfer data, (2) data caching on computational Resources, (3) use of a data-aware Resource selection scheduling algorithm similar to Storage Affinity, (4) a new Task selection scheduling algorithm (Temporal Tasks Grouping), based on the temporally grouped scheduling of Tasks sharing input data files. Data replication is also discussed. The proposed approach does not need an overlay network or Predictive Communications Ordering, making our operational implementation of a P2P Grid middleware easily deployable in unstructured P2P networks. Experiments show that performance gains are achieved by combining BitTorrent, caching, Storage Affinity and Temporal Tasks Grouping. This work can be summarized as combining P2P Grid computing and P2P data transfer technologies. [less ▲]

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See detailClosed-Loop Learning of Visual Control Policies
Jodogne, Sébastien ULg; Piater, Justus ULg

in Journal of Artificial Intelligence Research (2007), 28

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See detailClosed-Loop Learning of Visual Control Policies
JODOGNE, Sébastien ULg

Doctoral thesis (2006)

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See detailApproximate Policy Iteration for Closed-Loop Learning of Visual Tasks
Jodogne, Sébastien ULg; Briquet, Cyril ULg; Piater, Justus ULg

in Lecture Notes in Computer Science (2006, September), 4212

Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high- dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of ... [more ▼]

Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high- dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of mappings from images to actions. This approach requires a family of function approximators that maps visual percepts to a real-valued function. For this purpose, we use Regression Extra-Trees, a fast, yet accurate and versatile machine learning algorithm. The inputs of the Extra-Trees consist of a set of visual features that digest the informative patterns in the visual signal. We also show how to parallelize the Extra-Tree learning process to further reduce the computational expense, which is often essential in visual tasks. Experimental results on real-world images are given that indicate that the combination of API with Extra-Trees is a promising framework for the interactive learning of visual tasks. [less ▲]

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See detailTask-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions
Jodogne, Sébastien ULg; Piater, Justus ULg

in Lecture Notes in Computer Science (2006), 4212

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See detailRobust analysis of silhouettes by morphological size distributions
Barnich, Olivier ULg; JODOGNE, Sébastien ULg; Van Droogenbroeck, Marc ULg

in Lecture Notes in Computer Science (2006), 4179

We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as ... [more ▼]

We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image, our operator produces the set of all the largest rectangles that can be wedged inside any connected component of the image. The latter are obtained by a standard background subtraction technique and morphological filtering. To classify connected components into one of the known object categories, the rectangles of a connected component are submitted to a machine learning algorithm called EXtremely RAndomized trees (Extra-trees). The machine learning algorithm is fed with a static database of silhouettes that contains both positive and negative instances. The whole process, including image processing and rectangle classification, is carried out in real-time. Finally we evaluate our approach on one of today's hot topics: the detection of human silhouettes. We discuss experimental results and show that our method is stable and computationally effective. Therefore, we assess that algorithms like ours introduce new ways for the detection of humans in video sequences. [less ▲]

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See detailControlling an Agent by Focusing its Attention on Interactively Selected Patterns
JODOGNE, Sébastien ULg; Piater, Justus ULg

in HF Journal -- Belgian Journal of Electronics Communications (2005), (1), 14--16

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See detailAn effective decision procedure for linear arithmetic over the integers and reals
Boigelot, Bernard ULg; Jodogne, Sébastien ULg; Wolper, Pierre ULg

in ACM transactions on Computational Logic (2005), 6(3), 614--633

This article considers finite-automata-based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite ... [more ▼]

This article considers finite-automata-based algorithms for handling linear arithmetic with both real and integer variables. Previous work has shown that this theory can be dealt with by using finite automata on infinite words, but this involves some difficult and delicate to implement algorithms. The contribution of this article is to show, using topological arguments, that only a restricted class of automata on infinite words are necessary for handling real and integer linear arithmetic. This allows the use of substantially simpler algorithms, which have been successfully implemented. [less ▲]

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See detailLearning, then Compacting Visual Policies
Jodogne, Sébastien ULg; Piater, Justus ULg

in 7th European Workshop on Reinforcement Learning (2005)

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