Carlos F. Uribe

Orcid: 0000-0003-3127-7478

According to our database1, Carlos F. Uribe authored at least 10 papers between 2022 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.
CoRR, 2024

2023
Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.
Comput. Biol. Medicine, May, 2023

Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images.
CoRR, 2023

Observer study-based evaluation of TGAN architecture used to generate oncological PET images.
Proceedings of the Medical Imaging 2023: Image Perception, 2023

Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

2022
Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features.
CoRR, 2022

Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images.
Proceedings of the Medical Imaging 2022: Image Processing, 2022

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma.
Proceedings of the Medical Imaging 2022: Image Processing, 2022


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