Caroline Petitjean
Présentation
Publications
Publications
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On a Hybrid Joint Segmentation/Multimodal Registration Model via Finite Distortion Mappings and Implicit Neural RepresentationsScale Space and Variational Methods in Computer Vision, May 2025, Dartington, United Kingdom. pp.179-191, ⟨10.1007/978-3-031-92369-2_14⟩ |
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A Guided Tour of Post-hoc XAI Techniques in Image SegmentationExplainable Artificial Intelligence, Jul 2024, Malta, Malta. pp.155-177, ⟨10.1007/978-3-031-63797-1_9⟩ |
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Data usage and citation practices in medical imaging conferencesMedical Imaging with Deep Learning, Jul 2024, Paris, France |
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The Do's and Don'ts of Grad-CAM in Image Segmentation as demonstrated on the Synapse multi-organ CT DatasetMedical Imaging with Deep Learning, Jul 2024, Paris, France |
On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to RadiotherapySSVM 2023: Scale Space and Variational Methods in Computer Vision, May 2023, Santa Margherita di Pula (Cagliari), Italy. pp.363-375, ⟨10.1007/978-3-031-31975-4_28⟩ |
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Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation?27th Conference on Medical Image Understanding and Analysis 2023, Jul 2023, Aberdeen, United Kingdom. ⟨10.1007/978-3-031-48593-0_8⟩ |
Seg-XRes-CAM: Explaining Spatially Local Regions in Image Segmentation2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun 2023, Vancouver, France. pp.3733-3738, ⟨10.1109/CVPRW59228.2023.00384⟩ |
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A study of attention information from transformer layers in hybrid medical image segmentation networksSPIE Medical Imaging, Feb 2023, San Diego, United States. ⟨10.1117/12.2652215⟩ |
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A geometrically-constrained deep network for CT image segmentationIEEE International Symposium on Biomedical Imaging, Apr 2021, Nice (virtuel), France. ⟨10.1109/ISBI48211.2021.9434088⟩ |
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A Surprisingly Effective Perimeter-based Loss for Medical Image SegmentationFourth conference on Medical Imaging with Deep Learning (MIDL), Jul 2021, Lübeck, Germany |
Analysis of the weighted Van der Waals-Cahn-Hilliard model for image segmentation2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov 2020, Paris, France. pp.1-6, ⟨10.1109/IPTA50016.2020.9286628⟩ |
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SegTHOR: Segmentation of Thoracic Organs at Risk in CT images2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov 2020, Paris, France. pp.1-6, ⟨10.1109/IPTA50016.2020.9286453⟩ |
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Investigating CoordConv for Fully and Weakly Supervised Medical Image Segmentation2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov 2020, Paris, France. pp.1-5, ⟨10.1109/IPTA50016.2020.9286633⟩ |
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Explainability for regression CNN in fetal head circumference estimation from ultrasound imagesWorkshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, Oct 2020, Lima, Peru. pp.73-82, ⟨10.1007/978-3-030-61166-8_8⟩ |
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Organ Segmentation in CT Images With Weak Annotations: A Preliminary Study27-ème Colloque GRETSI sur le Traitement du Signal et des Images, Aug 2019, Lille, France |
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Pattern spotting in historical documents using convolutional models5th International Workshop on Historical Document Imaging and Processing, HIP 2019, Sydney, Australia, Sept 2019, ICDAR2019, Sep 2019, Sydney, Australia. pp.60-65, ⟨10.1145/3352631⟩ |
Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep ArchitecturesMICCAI’17 workshop Deep Learning in Medical Image Analysis, 2017, Quebec, Canada. pp.21-29 |
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Deep features for breast cancer histopathological image classification2017 IEEE International Conference on Systems, Man and Cybernetics (SMC), Oct 2017, Banff, Canada. pp.1868-1873 |
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Fully automated esophagus segmentation with a hierarchical deep learning approach2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Sep 2017, Kuching, Malaysia. pp.503-506 |
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SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDSIEEE Internation Symposium on Biomedical Imaging, Apr 2017, Melbourne, Australia. pp.1003-1006, ⟨10.1109/ISBI.2017.7950685⟩ |
Medical image synthesis with context-aware generative adversarial networksMiccai, Sep 2017, Quebec, Canada |
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Segmentation des organes á risque en imagerie scanner par architecture sharpmask et crf.GRETSI, 2017, Juan-Les-Pins, France |
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Polarization-based specularity removal method with global energy minimization2016 IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, United States. pp.1983-1987, ⟨10.1109/ICIP.2016.7532705⟩ |
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Region Proposal for Pattern Spotting in Historical Document Images2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Oct 2016, Shenzhen, China. pp.367-372 |
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Breast cancer histopathological image classification using Convolutional Neural Networks2016 International Joint Conference on Neural Networks (IJCNN), Jul 2016, Vancouver, Canada. pp.2560-2567 |
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Pattern localization in historical document images via template matching2016 23rd International Conference on Pattern Recognition (ICPR), Dec 2016, Cancun, Mexico. pp.2054-2059 |
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Segmentation-free pattern spotting in historical document images2015 13th International Conference on Document Analysis and Recognition (ICDAR), Aug 2015, Tunis, Tunisia. pp.606-610 |
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Détection de motifs graphiques dans des images de documents anciensGRETSI, 2015, Lyon, France |
Linear Discriminant Analysis for Zero-shot Learning Image RetrievalInternational Conference on Computer Vision Theory and Applications, Mar 2015, Berlin, Germany. pp.70-77 |
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Shape prior based image segmentation using manifold learningIEEE Image Processing, Tools and Applications, Nov 2015, Orléans, France |
Robust optimal feature selection for lung tumor recurrence prediction in PET imagingIn Annual Meeting of the American Society for Radiation Oncology, 2015, San Antonio, United States |
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Segmentation par coupes de graphe multi-labels avec a priori de formeReconnaissance de Formes et Intelligence Artificielle (RFIA) 2014, Jun 2014, France |
Joint Segmentation of Right and Left Cardiac Ventricles Using Multi-Label Graph CutIEEE International Symposium on Biomedical Imaging, Apr 2014, Beijing, China |
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3D automated lymphoma segmentation in PET images based on cellular automataInternational Conference on Image Processing Theory, Tools and Applications, 2014, Paris, France |
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Robust feature selection to predict tumor treatment outcome. Computational Methods for Molecular ImagingMICCAI Workshop, Sep 2014, Boston, United States |
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Segmentation automatique de tumeur par marche aléatoire basé sur un modèle de croissance de tumeurReconnaissance de formes et intelligence artificielle (RFIA) 2014, Jun 2014, France |
Robust Feature Selection to Predict Tumor Treatment OutcomeComputational Methods for Molecular Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention workshop, 2014, Boston, United States |
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Automatic Lung Tumor Segmentation on PET Images Based on Random Walks and Tumor Growth ModelIEEE International Symposium on Biomedical Imaging, Apr 2014, Beijing, China |
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Prediction lung tumor evolution during radiotherapy from pet images using a patient specific model.IEEE International Symposium on Biomedical Imaging, 2013, San Francisco, United States. pp.1404-7 |
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Prédiction de l’évolution de tumeurs pulmonaires en imagerie TEPColloque du Groupe d’Etudes du Traitement du Signal et des Images, 2013, Brest, France |
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A new random forest method for one class classificationJoint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Nov 2012, Hiroshima, Japan. pp.282-290 |
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Detection of pathological condition in distal lung imagesInternational Symposium on Biomedical Imaging, 2012, France |
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Segmentation d'images par coupe de graphe avec a priori de formeRFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), 2012, Lyon, France |
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Segmentation d'images par coupe de graphe avec a priori de formeRFIA, Jan 2012, Lyon, France. pp.CD-ROM |
Right ventricle segmentation in cardiac MRI : a MICCAI'12 challenge3D Cardiovascular Imaging : a MICCAI segmentation challenge, Oct 2012, Nice, France |
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Right ventricle segmentation by graph cut with shape prior3D Cardiovascular Imaging : a MICCAI segementation challenge, Oct 2012, Nice, France |
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A random forest based approach for one class classification in medical imaging3rd MICCAI International Workshop on Machine Learning in Medical Imaging (MLMI), 2012, Nice, France. pp.250-257 |
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Segmentation d'images par coupe de graphe avec a priori de formeRFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), Jan 2012, Lyon, France. pp.978-2-9539515-2-3 |
Segmentation d’images par coupe de graphe avec a priori de formeCongrès National sur la Reconnaissance de Formes et l’Intelligence Artificielle, 2012, Lyon, France |
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Manual vs automatic classification of endomicroscopic images of the distal lungProceedings of International Congress and exhibition on Computer Assisted Radiology and Surgery, 2011, Berlin, Germany |
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Using a priori knowledge to classify in vivo images of the lungInternational Conference on Intelligent Computing, Sep 2010, Changsha, China. pp.207-212 |
Classification d'images alvéoscopiques par extra-treesRFIA, Jan 2010, Caen, France |
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Using a priori knowledge to classify in vivo images of the lungInternational Conference on Intelligent Computing, 2010, Changsha, China. pp.207-212 |
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A Top-Down Approach for Automatic Dropper Extraction in Catenary ScenesIbPRIA, Jun 2009, Povoa de Varzim, Portugal. pp.225-232 |
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Extraction automatique de pendules dans des images de caténaire22ème Colloque GRETSI sur le Traitement du Signal et des Images, Sep 2009, Dijon, France. pp.296 |
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Automatic Extraction of Droppers in Catenary ScenesIAPR Conference on Machine Vision Applications, May 2009, Yokohama, Japan. pp.497-500 |
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Classification of In-vivo Endomicroscopic Images of the Alveolar Respiratory SystemIAPR Conference on Machine Vision Applications (MVA), May 2009, Yokohama, Japan. pp.471-474 |
Automatic classification of in vivo distal lung images for computer-aided diagnosisMedical Image Understanding and Analysis, Jul 2009, Londres, United Kingdom. pp.26 |
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Classification d'images endomicroscopiques du système respiratoire alvéolaire22ème Colloque GRETSI sur le Traitement du Signal et des Images, Sep 2009, Dijon, France. pp.299 |
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Characterization of endomicroscopic images of the distal lung for computer-aided diagnosisInternational Conference on Intelligent Computing, Sep 2009, Ulsan, South Korea. pp.994-1003 |
Automatic extraction of information for catenary scene analysisProceedings of the 16th European Signal Processing Conference (EUSIPCO'08), Aug 2008, Lausanne, Switzerland. pp.0 |
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Video based catenary inspection for preventive maintenance on iris 320.World Congress of Railway Research, May 2008, Séoul, South Korea |
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A statistical motion atlas for dynamically asessing myocardial function in MRIAnnual SCMR Scientific Sessions / Euro CMR 2004 Meeting, Feb 2004, Barcelona, Spain. pp.122-123 |
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Building and using a statistical 3D motion atlas for analyzing myocardial contraction in MRIConference on Image Processing - SPIE International Symposium Medical Imaging'04, Feb 2004, San Diego, CA, United States. pp.253-264 |
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A non rigid registration approach for measuring myocardial contraction in tagged MRI using exclusive f-informationICISP 2003 : International Conference on Image and Signal Processing, Jun 2003, Agadir, Morocco. pp.145 - 152 |
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Measuring myocardial deformations in tagged-MR image sequences using informational non rigid registrationSecond International Workshop on Functional Imaging and Modeling of the Heart (FIMH'2003), Jun 2003, Lyon, France. pp.162-172 |
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Variational non rigid image registration using exclusive f-informationInternational Conference on Image Processing (ICIP'2003), Sep 2003, Barcelona, Spain. pp.703-706 |
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Recalage variationnel non rigide d'images par f-information exclusiveActes 19e Colloque GRETSI sur le Traitement du Signal et des Images (GRETSI'2003), Sep 2003, Paris, France. pp.165-168 |
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Measuring myocardial deformations from MR data using information-theoretic nonrigid registrationCARS 2003 : 17th International Congress and Exhibition on Computer Assisted Radiology and Surgery, Jun 2003, Londres, United Kingdom. pp.1159 - 1164, ⟨10.1016/S0531-5131(03)00517-X⟩ |
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Measuring myocardial deformations from MR data using information-theoretic non rigid registration17th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS'2003), Jun 2003, London, United Kingdom. pp.1159-1164 |
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Automated 3D measurement of myocardial contraction from tagged MR sequences89th Scientific Assembly and Annual Meeting (RSNA'2003), Dec 2003, Chicago, United States. pp.414 |
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Benchmarking non-rigid registration techniques for the quantitative analysis of myocardial function in tagged-MR imagingEuropean Congress of Radiology 2002 (ECR'2002), Mar 2002, Vienna, Austria |
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Recalage non rigides par mesures d'information généraliséesJournée Thématique GDR MSPC, Apr 2002, Paris, France |
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Level set for face feature extraction: Application to lip trackingImage and Vision Computing New Zealand, Nov 2002, Auckland, New Zealand. pp.0 |
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Evaluation de techniques de recalage non rigide pour l'analyse quantitative de la fonction contractile myocardique en IRM de marquageJournées Francaises de Radiologie (JFR'2002), Oct 2002, Paris, France |
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Blending variational approaches and deep learning to enforce prior constraints in medical image segmentationLess-supervised Segmentation with CNNs, Elsevier, pp.217-235, 2026, ⟨10.1016/B978-0-32-395674-1.00017-2⟩ |
PRUNING TREES IN RANDOM FORESTS FOR MINIMIZING NON DETECTION IN MEDICAL IMAGINGHandbook of Pattern Recognition and Computer Vision, 5, WORLD SCIENTIFIC, pp.89-107, 2016 |
Assessment of myocardial function: A review of quantification methods and results using tagged MRI2003 |
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Non rigid image registration using generalized information measures2002 |
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Biomedical image analysis competitions: The state of current participation practice2025 |
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MiSuRe is all you need to explain your image segmentation2025 |
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Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images2013 |
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Recalage non rigide d'images par approches variationnelles statistiques. Application à l'analyse et à la modélisation de la fonction myocardique en IRMInformatique [cs]. Université René Descartes - Paris V, 2003. Français. ⟨NNT : ⟩ |
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Recalage non rigide d'images par approches variationnelles statistiques - Application à l'analyse et à la modélisation de la fonction myocardique en IRMInterface homme-machine [cs.HC]. Université René Descartes - Paris V, 2003. Français. ⟨NNT : ⟩ |