Search

Your search keyword '"Conze, Pierre-Henri"' showing total 10 results
Start Over Author "Conze, Pierre-Henri" Journal medical image analysis
10 results on '"Conze, Pierre-Henri"'

2. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge

Author

Ma, Jun, Zhang, Yao, Gu, Song, An, Xingle, Wang, Zhihe, Ge, Cheng, Wang, Congcong, Zhang, Fan, Wang, Yu, Xu, Yinan, Gou, Shuiping, Thaler, Franz, Payer, Christian, Štern, Darko, Henderson, Edward G.A., McSweeney, Dónal M., Green, Andrew, Jackson, Price, McIntosh, Lachlan, Nguyen, Quoc-Cuong, Qayyum, Abdul, Conze, Pierre-Henri, Huang, Ziyan, Zhou, Ziqi, Fan, Deng-Ping, Xiong, Huan, Dong, Guoqiang, Zhu, Qiongjie, He, Jian, and Yang, Xiaoping

Published
2022
Full Text
View/download PDF

3. CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Author

Al Hajj, Hassan, Lamard, Mathieu, Conze, Pierre-Henri, Roychowdhury, Soumali, Hu, Xiaowei, Maršalkaitė, Gabija, Zisimopoulos, Odysseas, Dedmari, Muneer Ahmad, Zhao, Fenqiang, Prellberg, Jonas, Sahu, Manish, Galdran, Adrian, Araújo, Teresa, Vo, Duc My, Panda, Chandan, Dahiya, Navdeep, Kondo, Satoshi, Bian, Zhengbing, Vahdat, Arash, Bialopetravičius, Jonas, Flouty, Evangello, Qiu, Chenhui, Dill, Sabrina, Mukhopadhyay, Anirban, Costa, Pedro, Aresta, Guilherme, Ramamurthy, Senthil, Lee, Sang-Woong, Campilho, Aurélio, Zachow, Stefan, Xia, Shunren, Conjeti, Sailesh, Stoyanov, Danail, Armaitis, Jogundas, Heng, Pheng-Ann, Macready, William G., Cochener, Béatrice, and Quellec, Gwenolé

Published
2019
Full Text
View/download PDF

8. CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

Author

Kavur, A. Emre, primary, Gezer, N. Sinem, additional, Barış, Mustafa, additional, Aslan, Sinem, additional, Conze, Pierre-Henri, additional, Groza, Vladimir, additional, Pham, Duc Duy, additional, Chatterjee, Soumick, additional, Ernst, Philipp, additional, Özkan, Savaş, additional, Baydar, Bora, additional, Lachinov, Dmitry, additional, Han, Shuo, additional, Pauli, Josef, additional, Isensee, Fabian, additional, Perkonigg, Matthias, additional, Sathish, Rachana, additional, Rajan, Ronnie, additional, Sheet, Debdoot, additional, Dovletov, Gurbandurdy, additional, Speck, Oliver, additional, Nürnberger, Andreas, additional, Maier-Hein, Klaus H., additional, Bozdağı Akar, Gözde, additional, Ünal, Gözde, additional, Dicle, Oğuz, additional, and Selver, M. Alper, additional

Published
2021
Full Text
View/download PDF

10. ExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis

Author

Gwenole Quellec, Béatrice Cochener, Pierre-Henri Conze, Hassan Al Hajj, Mathieu Lamard, Pascale Massin, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Institut National de la Santé et de la Recherche Médicale (INSERM), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Conze, Pierre-Henri, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
FOS: Computer and information sciences, Decision support system, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Health Informatics, 030218 nuclear medicine & medical imaging, Task (project management), Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Diabetes Mellitus, Photography, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, Mass Screening, Radiology, Nuclear Medicine and imaging, Relevance (information retrieval), Medical diagnosis, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Diabetic Retinopathy, Radiological and Ultrasound Technology, Pixel, business.industry, Novelty, Diabetic retinopathy, medicine.disease, Computer Graphics and Computer-Aided Design, ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Categorization, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Computer Vision and Pattern Recognition, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, 030217 neurology & neurosurgery
Abstract

In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the “black-box” nature of successful AI algorithms still holds back their wide-spread deployment. In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI, for the task of classifying Diabetic Retinopathy (DR) severity using Color Fundus Photography (CFP). This algorithm, called ExplAIn, learns to segment and categorize lesions in images; the final image-level classification directly derives from these multivariate lesion segmentations. The novelty of this explanatory framework is that it is trained from end to end, with image supervision only, just like black-box AI algorithms: the concepts of lesions and lesion categories emerge by themselves. For improved lesion localization, foreground/background separation is trained through self-supervision, in such a way that occluding foreground pixels transforms the input image into a healthy-looking image. The advantage of such an architecture is that automatic diagnoses can be explained simply by an image and/or a few sentences. ExplAIn is evaluated at the image level and at the pixel level on various CFP image datasets. We expect this new framework, which jointly offers high classification performance and explainability, to facilitate AI deployment.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results