Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images

Author:

Huang Zhengshan,Guo YuORCID,Zhang Ning,Huang Xian,Decazes Pierre,Becker Stephanie,Ruan SuORCID

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference48 articles.

1. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network;Blanc-Durand;Eur. J. Nucl. Med. Mol. Imag.,2021

2. A brief introduction to weakly supervised learning;Zhou;Natl. Sci. Rev.,2018

3. Weakly supervised cell instance segmentation under various conditions;Nishimura;Med. Image Anal.,2021

4. Constrained-CNN losses for weakly supervised segmentation;Kervadec;Med. Image Anal.,2019

5. A topological loss function for deep-learning based image segmentation using persistent homology;Clough;IEEE Trans. Pattern Anal. Mach. Intell.,2020

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1. MTR-PET: Multi-temporal resolution PET images for lymphoma segmentation;Biomedical Signal Processing and Control;2024-01

2. Deep learning for automatic tumor lesions delineation and prognostic assessment in multi-modality PET/CT: A prospective survey;Engineering Applications of Artificial Intelligence;2023-08

3. 3D PET/CT Tumor Co-Segmentation Based on Background Subtraction Hybrid Active Contour Model;International Journal of Pattern Recognition and Artificial Intelligence;2023-06-30

4. HC-Net: A hybrid convolutional network for non-human primate brain extraction;Frontiers in Computational Neuroscience;2023-02-09

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