Mireia Crispin-Ortuzar

Orcid: 0000-0002-4351-3709

According to our database1, Mireia Crispin-Ortuzar authored at least 23 papers between 2020 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma.
CoRR, April, 2026

ProGIS: Prototype-Guided Interactive Segmentation for Pathological Images.
IEEE Trans. Medical Imaging, March, 2026

CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis.
CoRR, February, 2026

A Systematic Review on Data-Driven Brain Deformation Modeling for Image-Guided Neurosurgery.
CoRR, February, 2026

Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma.
CoRR, January, 2026

PH2ST: Prompt-guided hypergraph learning for spatial transcriptomics prediction in whole slide images.
Medical Image Anal., 2026

2025
R<sup>2</sup>Seg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection.
CoRR, November, 2025

Multi-task deep learning for automatic image segmentation and treatment response assessment in metastatic ovarian cancer.
Int. J. Comput. Assist. Radiol. Surg., September, 2025

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer.
CoRR, August, 2025

ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction.
CoRR, March, 2025

CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.
Bioinform., 2025

Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer.
Proceedings of the Applications of Medical Artificial Intelligence, 2025

Probabilistic Integration of Renal Cancer Radiology and Pathology Using Graph Neural Networks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

2024
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration.
CoRR, 2024

CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis.
CoRR, 2024

A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer.
Proceedings of the Biomedical Image Registration - 11th International Workshop, 2024

Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024

Shallow-Deep Synergy: Boosting Cross-Domain Generalization in Histopathological Image Segmentation.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024

2023
Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation.
Comput. Biol. Medicine, September, 2023

Live Demonstration: An AI Driven Multiplexed Diagnostic Platform for Improving Cancer Care.
Proceedings of the 2023 IEEE SENSORS, Vienna, Austria, October 29 - Nov. 1, 2023, 2023

2022
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation.
CoRR, 2022

2021
Predictive Modelling of Highly Multiplexed Tumour Tissue Images by Graph Neural Networks.
Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data, 2021

2020
Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.
Comput. Biol. Medicine, 2020


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