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Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*.

, , , , , , , , , , и . EMBC, стр. 1536-1539. IEEE, (2020)

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Noémie Esnault

Noémie Debroux

Noémie-Fleur Sandillon-Rezer

Cynthia Moreau

Patrick Moreau

 

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Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge., , и . CoRR, (2019)Automatic classification of benign and malignant kidney masses using radiomics. A retrospective study exploiting the KiTS19 dataset., , , , и . Medical Imaging: Image Processing, том 11596 из SPIE Proceedings, SPIE, (2021)Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer*., , , , , , , , , и 1 other автор(ы). EMBC, стр. 1532-1535. IEEE, (2020)Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*., , , , , , , , , и 1 other автор(ы). EMBC, стр. 1536-1539. IEEE, (2020)Deformable Image Registration with Deep Network Priors: a Study on Longitudinal PET Images., , , , , , , , , и 3 other автор(ы). CoRR, (2021)Utilisation de l'apprentissage profond pour la segmentation et la caractérisation des images TEP/TDM FDG dans le cadre du cancer du sein métastatique. (Deep learning methods to segment and characterize PET/CT images in the context of metastatic breast cancer).. University of Nantes, France, (2022)Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images., , , , , , , , , и 1 other автор(ы). Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies., , , , , , , , , и 2 other автор(ы). EMBC, стр. 4736-4739. IEEE, (2022)