C. Lartizien CNRS Research Director
Research interest
I am a CNRS Research Director (section 7). I am conducting research at the CREATIS laboratory in Lyon, France, whose aim is to develop image processing methods for medical imaging.
My current research interests focus on machine learning methods for medical image analysis.
The clinical applications concern the prototyping of diagnosis and prognosis models for cancer and brain pathology based on multimodality medical imaging.
NEWS
[2022-02-03] Audrey Duran defended her PhD thesis on on deep models for prostate cancer mapping
[2021-10-01] Matthis Manthe was hired as a PhD student to work on federated learning for medical imaging in collaboration with Stefan Duffner from LIRIS lab granted by the ANR IADoc@UdL program
[2020-10-01] Nicolas Pinon was hired as a PhD student to develop self-supervised representation learning for anomaly detection in neuroimaging
[2019-11-01] Daria Zotova was hired as a PhD student on the TADALOT and ANR IMAGINA projects to develop efficient strategies to fuse multimodality neuroimaging data for deep predictive modeling with small data
[2019-01-03] Audrey Duran was hired as a PhD student on the PERFUSE RHU ANR project to design deep models for prostate cancer mapping.
[2019-01-03] Zaruhi Alaverdyan defended her PhD thesis on Unsupervised representation learning for anomaly detection on neuroimaging
[2018-12-01] Kick-off meeting of the ANR project entitled IMAGINA
[2017-12-01] Kick-off meeting of the ANR RHU project entitled PERFUSE
New publications
[2022-04-01] : Our paper on deep supervised attention models for prostate cancer segmentation and grading in mp-MRI was accepted for publication Medical Image Analysis [Duran, MEDIA 2022].
[2022-02-23] : Our paper on weakly supervised learning with scribble annotations for prostate cancer segmentation in mp-MRI was presented at the 2022 SPIE Medical Imaging Conference [Duran, SPIE MI 2022].
[2021-09-30] : Our paper on brain PET synthesis with cycle-GAN to train unsupervised deep anomaly detection model was presented at the 2021 MICCAI workshop [Zotova, MICCAI SASHIMI 2021].
[2021-09-30] : Our paper on unsupervised anomaly detection in MR brain scans of early Parkinsonian patients was presented at the 2021 MICCAI workshop [Munoz, MICCAI MLCN 2021]
on brain PET synthesis with cycle-GAN to train unsupervised deep anomaly detection model deep attention models for prostate segmentation in mp-MRI [Munoz, MICCAI MLCN 2021] .
[2020-07-01] : Please have a look at our two accepted papers at the 2020 MIDL conference on deep attention models for prostate cancer segmentation and grading in mp-MRI [Duran, MIDL 2020] and punctuate white matter lesions in 3D cranial ultrasonography of premature neonates [Erbacher, MIDL 2020].
[2020-01-01] : Our paper on unsupervised brain anomaly detection in multiparametric MRI based on siamese networks has been published in Medical Image Analysis. Please have a look here.
[2017-07-01] : Our paper on the generalization of the SVDD algorithm has been published in Neurocomputing. Please have a look here.
