Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Robin Camarasa

Robin Camarasa

for the ALFA study

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Aug 15, 2022
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther E. Bron, Robin Camarasa, Nish Chaturvedi, Yuan Chen, Zihao Chen, Shuai Chen, Qi Dou, Tavia Evans, Ivan Ezhov, Haojun Gao, Marta Girones Sanguesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, M. Arfan Ikram, Silvia Ingala, H. Rolf Jaeger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Minho Lee, Bo Li, Luigi Lorenzini, Bjoern Menze, Jose Luis Molinuevo, Yiwei Pan, Elodie Puybareau, Rafael Rehwald, Ruisheng Su, Pengcheng Shi, Lorna Smith, Therese Tillin, Guillaume Tochon, Helene Urien, Bas H. M. van der Velden, Isabelle F. van der Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius de Groot, Meike W. Vernooij, Marleen de Bruijne

Figure 1 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Figure 2 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Figure 3 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Figure 4 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Access Paper or Ask Questions

A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Sep 22, 2021
Robin Camarasa, Daniel Bos, Jeroen Hendrikse, Paul Nederkoorn, M. Eline Kooi, Aad van der Lugt, Marleen de Bruijne

Figure 1 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Figure 2 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Figure 3 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Figure 4 for A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

* 39 pages, 22 figures, to be published in Journal of Machine Learning for Biomedical Imaging for the Special Issue: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) 2020

Access Paper or Ask Questions

Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Aug 10, 2021
Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen Yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau

Figure 1 for Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Figure 2 for Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Figure 3 for Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Figure 4 for Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

* Submitted to Medical Image Analysis

Access Paper or Ask Questions