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3. Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction

Author

Gomez, Tristan, Feyeux, Magalie, Normand, Nicolas, David, Laurent, Paul-Gilloteaux, Perrine, Fréour, Thomas, and Mouchère, Harold

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

An important limitation to the development of Artificial Intelligence (AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a public reference benchmark to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 videos of developing embryos, for a total of 337k images. We applied ResNet, LSTM, and ResNet-3D architectures to our dataset and demonstrate that they overperform algorithmic approaches to automatically annotate stage development phases. Altogether, we propose the first public benchmark that will allow the community to evaluate morphokinetic models. This is the first step towards deep learning-powered IVF. Of note, we propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. We postulate that this original approach will help improve the overall performance of deep learning approaches on time-lapse videos of embryo development, ultimately benefiting infertile patients with improved clinical success rates (Code and data are available at https://gitlab.univ-nantes.fr/E144069X/bench_mk_pred.git).

Published
2022

7. Single Scan Granulometry Estimation from an Asymmetric Distance Map

Author

Normand, Nicolas, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Couprie, Michel, editor, Cousty, Jean, editor, Kenmochi, Yukiko, editor, and Mustafa, Nabil, editor

Published
2019
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8. Distance Transform Based on Weight Sequences

Author

Nagy, Benedek, Strand, Robin, Normand, Nicolas, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Couprie, Michel, editor, Cousty, Jean, editor, Kenmochi, Yukiko, editor, and Mustafa, Nabil, editor

Published
2019
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10. Recovering Missing Slices of the Discrete Fourier Transform using Ghosts

Author

Chandra, Shekhar, Svalbe, Imants, Guedon, Jeanpierre, Kingston, Andrew, and Normand, Nicolas

Subjects
Mathematical Physics, Computer Science - Discrete Mathematics, Mathematics - Combinatorics
Abstract

The Discrete Fourier Transform (DFT) underpins the solution to many inverse problems commonly possessing missing or un-measured frequency information. This incomplete coverage of Fourier space always produces systematic artefacts called Ghosts. In this paper, a fast and exact method for de-convolving cyclic artefacts caused by missing slices of the DFT is presented. The slices discussed here originate from the exact partitioning of DFT space, under the projective Discrete Radon Transform, called the Discrete Fourier Slice Theorem. The method has a computational complexity of O(n log2 n) (where n = N^2) and is constructed from a new Finite Ghost theory. This theory is also shown to unify several aspects of work done on Ghosts over the past three decades. The paper concludes with a significant application to fast, exact, non-iterative image reconstruction from sets of discrete slices obtained for a limited range of projection angles., Comment: 10 pages, 18 figures (submitted to IEEE Image Proc.)

Published
2010
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11. Fast Mojette Transform for Discrete Tomography

Author

Chandra, Shekhar S., Normand, Nicolas, Kingston, Andrew, Guédon, Jeanpierre, and Svalbe, Imants

Subjects
Physics - Medical Physics, Computer Science - Discrete Mathematics, Mathematics - Combinatorics
Abstract

A new algorithm for reconstructing a two dimensional object from a set of one dimensional projected views is presented that is both computationally exact and experimentally practical. The algorithm has a computational complexity of O(n log2 n) with n = N^2 for an NxN image, is robust in the presence of noise and produces no artefacts in the reconstruction process, as is the case with conventional tomographic methods. The reconstruction process is approximation free because the object is assumed to be discrete and utilizes fully discrete Radon transforms. Noise in the projection data can be suppressed further by introducing redundancy in the reconstruction. The number of projections required for exact reconstruction and the response to noise can be controlled without comprising the digital nature of the algorithm. The digital projections are those of the Mojette Transform, a form of discrete linogram. A simple analytical mapping is developed that compacts these projections exactly into symmetric periodic slices within the Discrete Fourier Transform. A new digital angle set is constructed that allows the periodic slices to completely fill all of the objects Discrete Fourier space. Techniques are proposed to acquire these digital projections experimentally to enable fast and robust two dimensional reconstructions., Comment: 22 pages, 13 figures, Submitted to Elsevier Signal Processing

Published
2010

12. Mojette Transform on Densest Lattices in 2D and 3D

Author

Ricordel, Vincent, Normand, Nicolas, Guédon, Jeanpierre, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kropatsch, Walter G., editor, Artner, Nicole M., editor, and Janusch, Ines, editor

Published
2017
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16. Le grand livre de l'Afrique : Chaos ou émergence au sud du Sahara ? - Préface d'Erik Orsenna Ed. 1

Author

Normand, Nicolas, Normand, Nicolas, Normand, Nicolas, and Normand, Nicolas

Abstract

"J'en ai lu des livres sur l'Afrique, depuis le temps que je l'aime et que je m'acharne à la comprendre. Peu d'entre eux m'ont autant éclairé."Érik Orsenna C'est en Afrique, désormais, que se jouent la démographie et la croissance de notre planète. Parmi les premières régions touchées par le réchauffement climatique, ce continent porte aussi en germe les drames humanitaires et écologiques de demain. À rebours des idées reçues, cet ouvrage dresse un panorama complet de la réalité africaine, pour comprendre le présent et appréhender l'avenir. Cette nouvelle édition mise à jour aborde successivement les questions de politique, d'économie et de culture, sans omettre les sujets d'actualité que sont le terrorisme, la pauvreté, l'aide au développement... Conçu par un ancien diplomate ayant consacré l'essentiel de sa carrière à l'Afrique, il constitue un outil unique de découverte et d'étude, agrémenté d'une douzaine de cartes.

Published
2022

18. Fourier Inversion of the Mojette Transform

Author

Kingston, Andrew, Li, Heyang, Normand, Nicolas, Svalbe, Imants, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Barcucci, Elena, editor, Frosini, Andrea, editor, and Rinaldi, Simone, editor

Published
2014
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19. Back-Projection Filtration Inversion of Discrete Projections

Author

Svalbe, Imants, Kingston, Andrew, Normand, Nicolas, Der Sarkissian, Henri, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Barcucci, Elena, editor, Frosini, Andrea, editor, and Rinaldi, Simone, editor

Published
2014
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21. A Weight Sequence Distance Function

Author

Nagy, Benedek, Strand, Robin, Normand, Nicolas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hendriks, Cris L. Luengo, editor, Borgefors, Gunilla, editor, and Strand, Robin, editor

Published
2013
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22. Digital Distances and Integer Sequences

Author

Normand, Nicolas, Strand, Robin, Evenou, Pierre, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gonzalez-Diaz, Rocio, editor, Jimenez, Maria-Jose, editor, and Medrano, Belen, editor

Published
2013
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23. Properties of Minimal Ghosts

Author

Svalbe, Imants, Normand, Nicolas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Debled-Rennesson, Isabelle, editor, Domenjoud, Eric, editor, Kerautret, Bertrand, editor, and Even, Philippe, editor

Published
2011
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24. Path-Based Distance with Varying Weights and Neighborhood Sequences

Author

Normand, Nicolas, Strand, Robin, Evenou, Pierre, Arlicot, Aurore, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Debled-Rennesson, Isabelle, editor, Domenjoud, Eric, editor, Kerautret, Bertrand, editor, and Even, Philippe, editor

Published
2011
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26. Artificial Intelligence Using Multimodal Data for Myelodysplastic Syndrome Screening

Author

De almeida Braga, Cedric, primary, Bauvais, Maxence, additional, Dominici, Maxence, additional, Garnier, Alice, additional, Peterlin, Pierre, additional, Debord, Camille, additional, Wuillème, Soraya, additional, Theisen, Olivier, additional, Godon, Catherine, additional, Bouzy, Simon, additional, Le Bris, Yannick, additional, Bene, Marie C, additional, Chevallier, Patrice, additional, Normand, Nicolas, additional, and Eveillard, Marion, additional

Published
2022
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27. Medial Axis LUT Computation for Chamfer Norms Using -Polytopes

Author

Normand, Nicolas, Évenou, Pierre, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Coeurjolly, David, editor, Sivignon, Isabelle, editor, Tougne, Laure, editor, and Dupont, Florent, editor

Published
2008
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29. A Geometry Driven Reconstruction Algorithm for the Mojette Transform

Author

Normand, Nicolas, Kingston, Andrew, Évenou, Pierre, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kuba, Attila, editor, Nyúl, László G., editor, and Palágyi, Kálmán, editor

Published
2006
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30. The Mojette Transform: The First Ten Years

Author

Guédon, JeanPierre, Normand, Nicolas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Andres, Eric, editor, Damiand, Guillaume, editor, and Lienhardt, Pascal, editor

Published
2005
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31. How to Obtain a Lattice Basis from a Discrete Projected Space

Author

Normand, Nicolas, Servières, Myriam, Guédon, JeanPierre, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Andres, Eric, editor, Damiand, Guillaume, editor, and Lienhardt, Pascal, editor

Published
2005
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32. Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies

Author

Moreau, Noemie, primary, Rousseau, Caroline, additional, Fourcade, Constance, additional, Santini, Gianmarco, additional, Ferrer, Ludovic, additional, Lacombe, Marie, additional, Guillerminet, Camille, additional, Colombie, Mathilde, additional, Jezequel, Pascal, additional, Campone, Mario, additional, Rubeaux, Mathieu, additional, and Normand, Nicolas, additional

Published
2022
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37. Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment

Author

Moreau, Noémie, primary, Rousseau, Caroline, additional, Fourcade, Constance, additional, Santini, Gianmarco, additional, Brennan, Aislinn, additional, Ferrer, Ludovic, additional, Lacombe, Marie, additional, Guillerminet, Camille, additional, Colombié, Mathilde, additional, Jézéquel, Pascal, additional, Campone, Mario, additional, Normand, Nicolas, additional, and Rubeaux, Mathieu, additional

Published
2021
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38. TOWARDS ACCURATE RATE ESTIMATION FOR 3D POINT CLOUD COMPRESSION BY TSPLVQ

Author

Filali, Amira, Ricordel, Vincent, Normand, Nicolas, Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
ratedistortion optimization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, compression, G-PCC, 3D point cloud
Abstract

International audience; Point clouds are widely emerged as a promising 3D visual representation model for immersive and high quality experiences using dense point cloud. However, as they are usually made up of thousands up to billions of points, advanced techniques of data compression are essential to store and transmit this type of data. This paper improves prior work, which provided a new top-down hierarchical geometry representation based on adaptive Tree-Structured Point-Lattice Vector Quantization (TSPLVQ), to make the point cloud geometry compression more adaptive to the input point cloud characteristics. In the paper, more robust Rate-Distortion optimization process is introduced to perform efficient and accurate rate-aware splitting decisions when building and coding the tree structure. Experimental results in geometry point cloud compression, considering the tree structures and the leaves coding, show that the solution takes advantage of well-established principles that have been paramount to reach higher levels of point cloud geometry compression performance.

Published
2021

39. Influence of expertise on human and machine visual attention in a medical image classification task

Author

Vallée, Rémi, Coutrot, Antoine, Normand, Nicolas, Mouchère, Harold, Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), and Université de Lyon-Université Lumière - Lyon 2 (UL2)

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; In many different domains, experts are able to solve complex tasks after glancing very briefly at an image (e.g. radiologists, pilots...). However the perceptual mechanisms underlying expert performance are still largely unknown. Recently, several machine learning algorithms have been shown to outperform human experts in specific tasks such as skin cancer classification. But similarly as humans, these algorithms often behave as black boxes and their information processing pipeline remains unknown. This lack of transparency and interpretability is highly problematic in applications involving human lives, such as healthcare. In this work, we directly compare human visual attention to machine visual attention when performing the same visual task. We have designed a medical diagnosis task involving the detection of lesions in 250 small bowels endoscopic images. We collected eye movements from 22 novices and gastroenterologists with various degrees of expertise while they classified these images according to their pathological status. In parallel, we trained a deep learning algorithm on the exact same task. We show that the post-hoc artificial attention maps (i.e. the image regions most used by the algorithm to take a decision) are significantly closer to human expert attention maps than to the ones of human novices. Interestingly, this is true for pathological images, but not for not pathological ones. Through the understanding of the similarities between the visual decision making process of human and machine experts, we hope to inform both the training of new doctors and the architecture of new algorithms.

Published
2021

40. Static and Dynamic 3D Point Cloud Compression by TSPLVQ

Author

Filali, Amira, Ricordel, Vincent, Normand, Nicolas, Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 3D dynamic point cloud, differential compression, G-PCC
Abstract

International audience; We have already addressed the issue of static point clouds geometry compression (G-PCC) by using Tree-Structured Point-Lattice Vector Quantization (TSPLVQ) [6]. Dynamic 3D point clouds are characterized by up millions of moving 3D positions and color attributes. Efficient point cloud (PC) compression is then fundamental. Temporally successive PC frames are very close, it however remains a challenging problem for coding as the PCs have varying numbers of points without explicit correspondence information. This paper improves and extends the prior work, which provided a new hierarchical geometry representation based on adaptive TSPLVQ. Firstly, a more robust Rate-Distortion optimization process is introduced in order to perform efficient and accurate rate-aware splitting decisions when building and coding the tree structure. Secondly, we focus on the compression of the geometry of dynamic point clouds (G-DPCC) and, the model enables to exploit the temporal dependencies of the 3D content. Exactly, TSPLVQ is a topdown method and permits to represent the PC geometry by using a scalable tree, so when quantizing the dynamic geometry of a given PCs sequence, the successive trees are represented as a trunk common for the 3D sequence, and branches added for each frame. Next, the trunk is first coded, followed by the branches that are differentially coded. Experimental results demonstrate that our method is able to bring significant improvement in terms of the overall compression performance compared to the state-of-the-art MPEG standard.

Published
2021

44. Multi-expert annotation of Crohn’s disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network

Author

de Maissin, Astrid, additional, Vallée, Remi, additional, Flamant, Mathurin, additional, Fondain-Bossiere, Marie, additional, Berre, Catherine Le, additional, Coutrot, Antoine, additional, Normand, Nicolas, additional, Mouchère, Harold, additional, Coudol, Sandrine, additional, Trang, Caroline, additional, and Bourreille, Arnaud, additional

Published
2021
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45. Deep Learning Based Blood Abnormalities Detection As a Tool for Vexas Syndrome Screening

Author

De Almeida Braga, Cedric, Bauvais, Maxence, Sujobert, Pierre, Heiblig, Mael, Jullien, Maxime, Le Calvez, Baptiste, Richard, Camille, Le Roc'h, Valentin, Rault, Emmanuelle, Herault, Olivier, Peterlin, Pierre, Garnier, Alice, Chevallier, Patrice, Bouzy, Simon, LE Bris, Yannick, Neel, Antoine, Graveleau, Julie, Paul-Gilloteaux, Perrine, Kosmider, Olivier, Normand, Nicolas, and Eveillard, Marion

Published
2023
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47. Le grand livre de l'Afrique : Histoire et société - Culture et institutions. - Politique et sécurité - Économie et développement Ed. 1

Author

Normand, Nicolas, Normand, Nicolas, Normand, Nicolas, and Normand, Nicolas

Abstract

"J'en ai lu des livres sur l'Afrique, depuis le temps que je l'aime et que je m'acharne à la comprendre. Peu d'entre eux m'ont autant éclairé." Érik Orsenna On ne peut plus négliger l'Afrique, car c'est là que se jouent la démographie et la croissance ou, sinon, les drames humanitaires et écologiques de demain. À rebours des idées reçues, cet ouvrage dresse un panorama complet de la réalité africaine, pour comprendre le présent et appréhender l'avenir. Il aborde successivement les questions de politique, d'économie et de culture, sans omettre les sujets d'actualité que sont le terrorisme, la pauvreté, l'aide au développement... Conçu par un ancien diplomate ayant consacré l'essentiel de sa carrière à l'Afrique, il constitue un outil unique de découverte et d'étude, agrémenté d'une douzaine de cartes.

Published
2018

48. Comparison between traditional and deep learning-based semi-automatic segmentation methods for metastatic breast cancer lesions monitoring

Author

Moreau, Noémie, Rousseau, Caroline, Ferrer, Ludovic, Campone, Mario, Colombié, Mathilde, Normand, Nicolas, Rubeaux, Mathieu, Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Keosys, Image Perception Interaction (LS2N - équipe IPI), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), and UNICANCER

Subjects
[SDV.CAN]Life Sciences [q-bio]/Cancer, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2020

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