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Pierre Decazes
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2020 – today
- 2025
- [j6]Ling Huang
, Su Ruan, Pierre Decazes, Thierry Denoeux
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025) - 2023
- [c12]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Hua Li, Pierre Vera, Pierre Decazes, Su Ruan:
Prediction of Head-Neck Cancer Recurrence from Pet/CT Images with Havrda-Charvat Entropy. IPTA 2023: 1-5 - [c11]Lixin Zhang, Yulun Sun, Pierre Decazes, Su Ruan, Yu Guo
One-shot Learning for DLBCL Segmentation in Whole Body PET/CT Images. ISBI 2023: 1-4 - [i6]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - 2022
- [j5]Zhengshan Huang, Yu Guo
Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Comput. Biol. Medicine 151(Part): 106230 (2022) - [j4]Ling Huang
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [c10]Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. HECKTOR@MICCAI 2022: 1-30 - [i5]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - 2021
- [c9]Ling Huang
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c8]Ling Huang
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [i4]Ling Huang
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i3]Ling Huang
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021) - [i2]Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim:
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics. CoRR abs/2110.10332 (2021) - 2020
- [c7]Amine Amyar
RADIOGAN: Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images. ICBRA 2020: 28-33 - [c6]Yu Guo
Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction. ISBI 2020: 1-4 - [c5]Haigen Hu, Leizhao Shen, Tongxue Zhou
Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy. ISBI 2020: 1197-1200 - [i1]Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski:
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images. CoRR abs/2003.08663 (2020)
2010 – 2019
- 2019
- [j3]Haigen Hu
Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy. Int. J. Comput. Assist. Radiol. Surg. 14(10): 1715-1724 (2019) - [j2]Pierre Decazes
Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT. J. Digit. Imaging 32(2): 241-250 (2019) - [c4]Haigen Hu, Chao Du, Pierre Decazes, Pierre Vera, Su Ruan:
A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs. BIBM 2019: 2413-2420 - [c3]Haigen Hu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
Gaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images. CISP-BMEI 2019: 1-5 - 2018
- [j1]Yuntao Yu, Pierre Decazes
Semi-automatic lymphoma detection and segmentation using fully conditional random fields. Comput. Medical Imaging Graph. 70: 1-7 (2018) - [c2]Jierui Zha, Pierre Decazes, Jérôme Lapuyade, Abderrahim Elmoataz, Su Ruan
3D lymphoma detection in PET-CT images with supervoxel and CRFs. IPTA 2018: 1-5 - 2017
- [c1]Yuntao Yu, Pierre Decazes, Isabelle Gardin, Pierre Vera, Su Ruan
3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs. CMMI/RAMBO/SWITCH@MICCAI 2017: 3-12
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