Pierre Decazes

According to our database1, Pierre Decazes authored at least 14 papers between 2017 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


On csauthors.net:


Lymphoma segmentation from 3D PET-CT images using a deep evidential network.
Int. J. Approx. Reason., 2022

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics.
CoRR, 2021

Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation.
Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

Evidential Segmentation of 3D PET/CT Images.
Proceedings of the Belief Functions: Theory and Applications, 2021

Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

RADIOGAN: Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images.
Proceedings of the ICBRA 2020: 7th International Conference on Bioinformatics Research and Applications, 2020

Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT.
J. Digit. Imaging, 2019

Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy.
Int. J. Comput. Assist. Radiol. Surg., 2019

Gaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images.
Proceedings of the 12th International Congress on Image and Signal Processing, 2019

A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

Semi-automatic lymphoma detection and segmentation using fully conditional random fields.
Comput. Medical Imaging Graph., 2018

3D lymphoma detection in PET-CT images with supervoxel and CRFs.
Proceedings of the Eighth International Conference on Image Processing Theory, 2018

3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs.
Proceedings of the Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment, 2017