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49

Responsible AI for high-content biomedical image analysis


Biography

Professor in BioMedical Image, Pattern Recognition, Machine / Deep Learning as Information and Data Analytics at Sorbonne University, and PI at the Paris Brain Institute (Stakeholders: CNRS UMR7225 - French National Center for Scientific Research, Inserm U1127-French National Institute of Health and Medical Research, APHP & Sorbonne University) - INRIA team “Aramis Lab” , my research is mainly focusing on microscopic biomedical image analysis, pattern recognition (including machine & deep learning) and computational integrative pathology.

Dr.habil. (2006) and Ph.D. (1997) at Univ. of Franche-Comté, Besançon, France, M.Sc. (DEA, 1993) at the University of Besançon, France and Engineer's degree (M.Eng. / Dipl.-Ing., 1992) at the “Politehnica” University from Timisoara, Romania, I was Project Manager at General Electric Energy Products - Europe, before joining, in 1999, a chair of Associate Professor at University of Besançon, France with Research Fellowship at the FEMTO-ST Institute (CNRS UMR6174).

From 2009 to 2015, I was Full Professor at the School of Computing, National University of Singapore[1].  From 2005 to 2014, I actively participated to the development (up to the UMI CNRS level in 2007) and the management of the International Joint Research Unit “Image & Pervasive Access Lab” (IPAL) - CNRS UMI2955, being the Director (2008-2014) of this international research venture created with the support of the CNRS, the National University of Singapore (NUS), the Agency for Science, Technology and Research (A*STAR), the Univ. Grenoble Alpes, in Singapore. Active in scientific collaborations between France and Singapore, I participated to the creation (co-President: 2010-2014) of the R&D committee of the French Chamber of Commerce in Singapore (FCCS).

In 2012, with my team and under my leadership, we organized the first international medical challenge/benchmark in digital pathology. This challenge, entitled “Mitosis Detection in Breast Cancer Histological Images” (MITOS 2012), was held in the framework of the 21st International Conference on Pattern Recognition - ICPR 2012 (Tsukuba, Japan). After this first success, we organized a second event, in the area of Atypia assessment in Breast Cancer Histological Images (ATYPIA 2014), in the framework of ICPR 2014 (Stockholm, Sweden). These initiatives pathed the way towards a translational digital pathology, on the move now, towards a daily use in routine diagnostic (the first USA FDA approval came out in 2017, in this sense).

From 2018 to 2020, I was the President of the European Society of Digital and Integrative Pathology[2], participating to the creation (2016) and the growth of this academic society. This is a continuation of my consistent involvement in this area, since the early stages of its development (vice-president from 2016 to 2018). In this context, I have been involved in the organization of the European Conference of Digital Pathology in Paris (ECDP 2014) and consistently supporting and participating to ECDP conferences, since.

Between 2014 and 2016, I was a member of the first Executive Board of the University Institute of Health Engineering of the Sorbonne University, being also co-Director and co-initiator of a new B.Sc. Minor, dedicated to Innovation in Public Health. During the same period, I was leading the Cancer Theranostics research team at the Bioimaging Lab, a joint research unit created between Sorbonne University, CNRS and Inserm - CNRS UMR7371, Inserm U1146.

From 2016 to 2018, I was Full Professor at the Pontifical Catholic University of Peru, being able to attract MICCAI 2020[3] (4-8 Oct. 2020) to Lima, Peru – for the first time in Latin America. I was the general co-chair of this conference. Since 2018, I am also a member of MICCAI (Medical Image Computing & Computer Assisted Intervention) Board of Directors[4].



[1] National University of Singapore (NUS) - top Public university in Asia, ranked #11 in QS Global World Rankings 2021: https://www.topuniversities.com/universities/national-university-singapore-nus

[2] ESDIP- European Society of Digital Integrative Pathologyhttp://digitalpathologysociety.org

 


Journal articles22 documents

  • Songhui Diao, Yinli Tian, Wanming Hu, Jiaxin Hou, Ricardo Lambo, et al.. Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network. American Journal of Pathology, American Society for Investigative Pathology, 2022, 192 (3), pp.553-563. ⟨10.1016/j.ajpath.2021.11.009⟩. ⟨hal-03677649⟩
  • Filippo Fraggetta, Vincenzo L’imperio, David Ameisen, Rita Carvalho, Sabine Leh, et al.. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics, MDPI, 2021, 11 (11), pp.2167. ⟨10.3390/diagnostics11112167⟩. ⟨hal-03456592⟩
  • Gabriel Jiménez, Daniel Racoceanu. Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading. Frontiers in Bioengineering and Biotechnology, Frontiers, 2019, 7, pp.145. ⟨10.3389/fbioe.2019.00145⟩. ⟨hal-02182488⟩
  • Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu. Deep Learning in the Biomedical Applications: Recent and Future Status. Applied Sciences, MDPI, 2019, 9 (8), pp.1526. ⟨10.3390/app9081526⟩. ⟨hal-02170880⟩
  • Monjoy Saha, Chandan Chakraborty, Daniel Racoceanu. Efficient deep learning model for mitosis detection using breast histopathology images. Computerized Medical Imaging and Graphics, Elsevier, 2018, 64, pp.29-40. ⟨10.1016/j.compmedimag.2017.12.001⟩. ⟨hal-03140981⟩
  • Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, et al.. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA Cardiology, American Medical Association 2017, 318, ⟨10.1001/jama.2017.14585⟩. ⟨hal-03140979⟩
  • K. Traore, C. Daniel, M.-C. Jaulent, T. Schrader, Daniel Racoceanu, et al.. Sustainable Formal Representation Of Breast Cancer Grading Histopathological Knowledge. Diagnostic Pathology, BioMed Central, 2016, 9 (1), ⟨10.17629/www.diagnosticpathology.eu-2016-8:154⟩. ⟨hal-01366742⟩
  • Jean-Baptiste Yunès, Daniel Racoceanu, David Ameisen, A. Veillard, B. Ben Cheikh, et al.. Towards Efficient Collaborative Digital Pathology: A Pioneer Initiative Of The FlexMIm Project. the diagnostic pathology journal, 2016. ⟨hal-02419474⟩
  • Daniel Racoceanu, Frédérique Capron. Semantic Integrative Digital Pathology: Insights on Microsemiological Semantics and Image Analysis Scalability. Pathobiology, Karger, 2016, 83 (2-3), pp.148-155. ⟨10.1159/000443964⟩. ⟨hal-01365808⟩
  • Sreetama Basu, Wei Tsang Ooi, Daniel Racoceanu. Neurite Tracing With Object Process. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2016, 35 (6), pp. 1443-1451. ⟨10.1109/TMI.2016.2515068⟩. ⟨hal-01366490⟩
  • Daniel Racoceanu, F Capron. Towards semantic-driven high-content image analysis: An operational instantiation for mitosis detection in digital histopathology. Computerized Medical Imaging and Graphics, Elsevier, 2015, 42, pp.2-15. ⟨10.1016/j.compmedimag.2014.09.004⟩. ⟨hal-01139965⟩
  • Fagette Antoine, Nicolas Courty, Daniel Racoceanu, Jean-Yves Dufour. Unsupervised dense crowd detection by multiscale texture analysis. Pattern Recognition Letters, Elsevier, 2013, pp.1-27. ⟨hal-00904210⟩
  • Nicolas Loménie, Daniel Racoceanu. Point Sets Morphological Filtering and Semantic Spatial Configurations Modeling: application to microscopic image analysis. Pattern Recognition, Elsevier, 2012, 45 (8), pp.2894-2911. ⟨10.1016/j.patcog.2012.01.021⟩. ⟨hal-00873430⟩
  • Mounir Mokhtari, Hamdi Aloulou, Thibaut Tiberghien, Jit Biswas, Daniel Racoceanu, et al.. New Trends to Support Independence in Persons with Mild Dementia-A Mini-Review. Gerontology, Karger, 2012, pp.10.1159/000337827. ⟨hal-00739829⟩
  • Chao-Hui Huang, Antoine Veillard, Nicolas Lomenie, Daniel Racoceanu, Ludovic Roux. Time-efficient sparse analysis of histopathological Whole Slide Images. Computerized Medical Imaging and Graphics, Elsevier, 2010, pp.5. ⟨hal-00553877⟩
  • Wei Xiong, S.H. Ong, Joo-Hwee Lim, Kelvin Foong Weng Chiong, Jiang Liu, et al.. Automatic Working Area Classification in Peripheral Blood Smears. IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2010, 57 (8), pp.1982-1990. ⟨hal-00553282⟩
  • Adrien Depeursinge, Daniel Racoceanu, Jimison Iavindrasana, Gilles Cohen, Alexandra Platon, et al.. Fusing Visual and Clinical Information for Lung Tissue Classification in HRCT Data. Artificial Intelligence in Medicine, Elsevier, 2010, pp.ARTMED1118. ⟨hal-00493108⟩
  • Nicolas Palluat, Daniel Racoceanu, Noureddine Zerhouni. A neuro-fuzzy monitoring system. Application to flexible production systems.. Computers in Industry, Elsevier, 2006, 57 (6), pp.528-538. ⟨10.1016/j.compind.2006.02.013⟩. ⟨hal-00263918⟩
  • Ryad Zemouri, Daniel Racoceanu, Nourredine Zerhouni. Réseaux de neurones récurrents à fonctions de base radiales. Application à la surveillance dynamique. Journal Européen des Systèmes Automatisés (JESA), Lavoisier, 2003, 37 (1), pp.49-81. ⟨10.3166/jesa.37.49-81⟩. ⟨hal-02479204⟩
  • Ryad Zemouri, Daniel Racoceanu, Nourredine Zerhouni. Réseaux de neurones récurrents à fonctions de base radiales : RRFR Application au pronostic. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2003, 16 (3), pp.307-338. ⟨10.3166/ria.16.307-338⟩. ⟨hal-02479188⟩
  • Ryad Zemouri, Daniel Racoceanu, Noureddine Zerhouni. Recurrent radial basis function network for time-series prediction. Engineering Applications of Artificial Intelligence, Elsevier, 2003, 16 (5-6), pp.453-463. ⟨10.1016/S0952-1976(03)00063-0⟩. ⟨hal-02479206⟩
  • Ryad Zemouri, Racoceanu Daniel, Zerhouni Noureddine. APPLICATION OF THE DYNAMIC RBF NETWORK IN A MONITORING PROBLEM OF THE PRODUCTION SYSTEMS. IFAC Proceedings Volumes, Elsevier, 2002, 35 (1), pp.295-300. ⟨10.3182/20020721-6-ES-1901.01602⟩. ⟨hal-02479197⟩

Conference papers17 documents

  • Kristyna Maňoušková, Valentin Abadie, Mehdi Ounissi, Gabriel Jimenez, Lev Stimmer, et al.. Tau Protein Discrete Aggregates in Alzheimer's Disease: Neuritic Plaques and Tangles Detection and Segmentation using Computational Histopathology. SPIE Medical Imaging 2022, Feb 2022, San Diego, United States. ⟨hal-03522378v2⟩
  • Fedra Trujillano, Jessenia Gonzalez, Carlos Saito, Andres Flores, Racoceanu Daniel, et al.. Corn Crops Identification Using Multispectral Images from Unmanned Aircraft Systems. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Jul 2021, Brussels, France. pp.4712-4715, ⟨10.1109/IGARSS47720.2021.9553826⟩. ⟨hal-03677650⟩
  • Chao-Hui Huang, Daniel Racoceanu. Enhanced Methods for Lymphocyte Detection and Segmentation on H&E Stained Images using eXclusive Autoencoders. IEEE EMBC'20 - 42nd Engineering in Medicine and Biology Conference, Jul 2020, Montreal / Virtual, Canada. ⟨hal-03140992⟩
  • Oumeima Laifa, Delphine Le Guillou-Buffello, Daniel Racoceanu. Tumor angiogenesis assessment using multi-fluorescent scans on murine slices by Markov Random Field framework. SIPAIM, 2017, San Andres Island - Colombia, Colombia. ⟨hal-01615267⟩
  • Daniel Salas, Jens Gustedt, Daniel Racoceanu, Isabelle Perseil. Resource-Centered Distributed Processing of Large Histopathology Images. 19th IEEE International Conference on Computational Science and Engineering, Aug 2016, Paris, France. ⟨hal-01325648⟩
  • Bassem Ben Cheikh, Philippe Bertheau, Daniel Racoceanu. Preliminary approach for crypt detection in Inflammatory Bowel Disease. Journées RITS 2015, Mar 2015, Dourdan, France. pp.138-139. ⟨inserm-01144091⟩
  • Stéphane Rigaud, Chao-Hui Huang, Sohail Ahmed, Joo-Hwee Lim, Daniel Racoceanu. An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul 2013, Osaka, France. pp.3989-3992, ⟨10.1109/EMBC.2013.6610419⟩. ⟨hal-02626861⟩
  • Stéphane Rigaud, Nicolas Loménie, Shvetha Sankaran, Sohail Ahmed, Joo-Hwee Lim, et al.. Neurosphere fate prediction: An analysis-synthesis approach for feature extraction. 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane), Jun 2012, Brisbane, Australia. pp.1-7, ⟨10.1109/IJCNN.2012.6252628⟩. ⟨hal-02626907⟩
  • Gilles Le Naour, Catherine Genestie, Ludovic Roux, Antoine Veillard, Daniel Racoceanu, et al.. Un explorateur visuel cognitif (MIcroscope COgnitif - MICO) pour l'histopathologie. Application au diagnostic et à la graduation du cancer du sein.. RITS 2011, Recherche en Imagerie et Technologies pour la Santé, Apr 2011, Rennes, France. pp.119. ⟨hal-00663408⟩
  • Nicolas Lomenie, Daniel Racoceanu. Spatial Relationships over Sparse Representations. Spatial Relationships over Sparse Representations, IVCNZ 2009 - Image and Vision Computing, Nov 2010, Wellington, New Zealand. ⟨hal-00497811⟩
  • Ryad Zemouri, Daniel Racoceanu, Noureddine Zerhouni, Eugénia Minca, Florin Gheorghe Filip. Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction. Intelligent systems and automation: 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA’09), Mar 2009, Zarzis (Tunisia), France. pp.85-90, ⟨10.1063/1.3106518⟩. ⟨hal-02479235⟩
  • Sorina Camarasu-Pop, H. Benoit-Cattin, J. Montagnat, D. Racoceanu. Grids for Content-Based Medical Image Indexing and Retrieval. ICT4Health, Oncomedia, 2008, Manila, The, Philippines. ⟨hal-01950484⟩
  • Adina Eunice Tutac, Daniel Racoceanu, Thomas Putti, Wei Xiong, Wee-Kheng Leow, et al.. Knowledge-Guided Semantic Indexing of Breast Cancer Histopathology Images. BMEI2008, International Conference on BioMedical Engineering and Informatics, May 2008, Sanya, Hainan, China. ⟨hal-00342275⟩
  • Eugénia Minca, Daniel Racoceanu, Florin Dragomir, Noureddine Zerhouni. A fuzzy approach for discrete event systems recovery.. IFAC - International Federation of Automatic Control. 4th IFAC Conference on Management and Control of Production and Logistics, MCPL'2007., Sep 2007, Sibiu, Romania. pp.585-590. ⟨hal-00189140⟩
  • Caroline Lacoste, Jean-Pierre Chevallet, Joo-Hwee Lim, Wei Xiong, Daniel Raccoceanu, et al.. IPAL Knowledge-based Medical Image Retrieval in ImageCLEFmed 2006. Working Notes for the CLEF 2006 Workshop, 20-22 September Medical Image Track, 2006, Alicante, Spain. ⟨hal-00954109⟩
  • Ryad Zemouri, D. Racoceanu, N. Zerhouni. From the spherical to an elliptic form of the dynamic RBF neural network influence field. 2002 International Joint Conference on Neural Networks (IJCNN), May 2002, Honolulu, United States. pp.107-112, ⟨10.1109/IJCNN.2002.1005452⟩. ⟨hal-02479186⟩
  • Ryad Zemouri, D. Racoceanu, N. Zerhouni. A Petri nets graphic method of reduction using birth-death processes. 2001 ICRA. IEEE International Conference on Robotics and Automation, May 2001, Seoul, South Korea. pp.46-51, ⟨10.1109/ROBOT.2001.932528⟩. ⟨hal-02479177⟩

Book sections1 document

  • Ryad Zemouri, Daniel Racoceanu. Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization. Mourad Elloumi. Deep Learning for Biomedical Data Analysis. Techniques, Approaches, and Applications, Springer International Publishing, pp.171-196, 2021, 978-3-030-71675-2. ⟨10.1007/978-3-030-71676-9_8⟩. ⟨hal-03524662⟩

Directions of work or proceedings7 documents

  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII (brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, Part VII - brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography (12267), Springer Chaim, pp.XXXVII, 817, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 Proceedings, 978-3-030-59728-3. ⟨hal-03144845⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV (segmentation; shape models and landmark detection). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, Proceedings, Part IV - segmentation; shape models and landmark detection (12264), Springer Cham, pp.XXXVII, 831, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 978-3-030-59719-1. ⟨hal-03144840⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I (machine learning methodologies). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, Proceedings, Part I - machine learning methodologies (12261), Springer Cham, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 978-3-030-59710-8. ⟨10.1007/978-3-030-59710-8⟩. ⟨hal-03144837⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III (CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, Part III - CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis (12263), Springer Cham, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 Proceedings, 978-3-030-59716-0. ⟨10.1007/978-3-030-59716-0⟩. ⟨hal-03144839⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V (biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V (12265), Springer Cham, pp.XXXVII, 811, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 978-3-030-59722-1. ⟨hal-03144842⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (12266), Springer Cham, pp.XXXVII, 819, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 978-3-030-59725-2. ⟨hal-03144844⟩
  • Anne L Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A Zuluaga, et al.. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II (image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks). Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2020, Lima, Peru. Lecture Notes in Computer Science, Proceedings, Part II - image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks (12262), Springer, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 978-3-030-59713-9. ⟨10.1007/978-3-030-59713-9⟩. ⟨hal-03144838⟩

Preprints, Working Papers, ...1 document

  • Roxana Teodorescu, Daniel Racoceanu, Wee-Kheng Leow, Vladimir Cretu. Prospective Study for Semantic Inter-Media Fusion in Content-Based Medical Image Retrieval. 2008. ⟨hal-00342276⟩

Habilitation à diriger des recherches1 document

  • Daniel Racoceanu. Contribution à la Surveillance des Systèmes de Production en Utilisant l'Intelligence Artificielle. Automatique / Robotique. Université de Franche-Comté, 2006. ⟨tel-00011708⟩