Paolo Soda
0000-0003-2621-072X
Umeå University
117 papers found
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Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer
Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features
RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy
3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study
MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer
A Multimodal Ensemble Driven by Multiobjective Optimisation to Predict Overall Survival in Non-Small-Cell Lung Cancer
CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI
Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study
Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review
PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction
On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior
3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients
Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest
Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting
Time-Window SIQR Analysis of COVID-19 Outbreak and Containment Measures in Italy
On Using Meta-Features to Learn Under Class Skew in Biomedical Domains
Radiomics-Based Non-Invasive Lymph Node Metastases Prediction in Breast Cancer
The new era of LED microscopes in immunofluorescence anti-nuclear antibody (ANA) testing
Preface: CBMS 2020
AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics
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