Nanna Maria Sijtsema

Orcid: 0000-0001-6644-274X

According to our database1, Nanna Maria Sijtsema authored at least 12 papers between 2021 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.
Comput. Methods Programs Biomed., 2024

2023
The Hidden Adversarial Vulnerabilities of Medical Federated Learning.
CoRR, 2023

Tackling Heterogeneity in Medical Federated learning via Vision Transformers.
CoRR, 2023

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks.
CoRR, 2023

Exploring adversarial attacks in federated learning for medical imaging.
CoRR, 2023

A Comparative Study of Federated Learning Models for COVID-19 Detection.
CoRR, 2023

TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer.
Proceedings of the Medical Imaging with Deep Learning, 2023

2022
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images.
CoRR, 2022

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022

2021
Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021


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