Scientific Publications

We’re making our research findings available free of charge for readers and are providing open access to published papers and reports. The list will be updated as the project progresses.

Title	Author	Journal	Date	DOI
Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging	C. M. Pirkel et al.	Neuroradiology	09/04/2021	10.1007/s00234-021-02703-0
Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping	C. M. Pirkl et al.	Proceedings of Machine Learning Research	09/07/2021	2021.midl.io/proceedings/pirkl21
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods	S. A. Mali et al.	Journal of Personalised Medicine	27/8/2021	10.3390/jpm11090842
Transparency of deep neural networks for medical image analysis: A review of interpretability methods	Z. Salahuddin et al.	Computers in Biology and Medicine	04/12/2021	10.1016/j.compbiomed.2021.105111
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions	Y. Nan et al.	Information Fusion	24/1/2022	10.1016/j.inffus.2022.01.001
Learning residual motion correction for fast and robust 3D multiparametric MRI	C. M. Pirkel et al.	Medical Image Analysis	07/02/2022	10.1016/j.media.2022.102387
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools	L. Martí Bonmatí et al.	Frontiers in Oncology	24/02/2022	10.3389/fonc.2022.742701
Quantitative Parameter Mapping of Prostate using Stack-of-Stars and QTI Encoding	R. Schulte et al.	Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2022	09/05/2022	https://index.mirasmart.com/ISMRM2022/PDFfiles/0182.html
Discussion Paper: The Integrity of Medical AI	Y. Mirsky	WDC ’22: Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes	30/05/2022	10.1145/3494109.3527191
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network	Y. Nan et al.	IEEE Transactions on Medical Imaging	29/07/2022	10.1109/TMI.2022.3195123
FUTURE- AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare	L. Martí-Bonmatí et al.	The BMJ	05/02/2025	https://doi.org/10.1136/bmj-2024-081554
Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!	L. Martí-Bonmatí et al.	European Radiology Experimental	15/02/2025	https://doi.org/10.1186/s41747-024-00543-0

Publications with CHAIMELEON contribution

Title	Author	Journal	Date	DOI
Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures	S. Sanduleanu et al.	Radiotherapy and Oncology	30/10/2020	10.1016/j.radonc.2020.10.016
3D PBV-Net: An automated prostate MRI data segmentation method	Y. Jin, G. Yang et al.	Computers in Biology and Medicine	7/12/2020	10.1016/j.compbiomed.2020.104160
Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma	M. Verduin et al.	Cancers	10/02/2021	10.3390/cancers13040722
ME-Net: Multi-encoder net framework for brain tumor segmentation	W. Zhang, G. Yang et al.	International Journal of Imaging Systems and Technology	07/3/2021	10.1002/ima.22571
Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients	R. Casale et al.	European Journal of Radiology	05/04/2021	10.1016/j.ejrad.2021.109678
Estimaciones de causalidad con imagen médica en oncología/Estimates of Causality with Medical Image in Oncology	L. Martí-Bonmati	Anales RANM	22/4/2021	10.32440/ar.2021.138.01.rev02
A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images	S. A. Keek et al.	Cancers	29/06/2021	10.3390/cancers13133271
MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study	R. W. Y. Granzier et al.	Cancers	18/05/2021	10.3390/cancers13102447
A Deep Multi-Task Learning Framework for Brain Tumor Segmentation	H. Huang et al.	Frontiers in Oncology	04/06/2021	10.3389/fonc.2021.690244
A deep look into radiomics	C. Scapicchio et al.	La radiologia medica	02/07/2021	10.1007/s11547-021-01389-x
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication	O. Morin et al.	Nature Cancer	22/07/2021	10.1038/s43018-021-00236-2
Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data	A. Ibrahim et al.	Cancers	16/09/2021	10.3390/cancers13184638
Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification	Y. Liu et al.	Diagnostics	28/09/2021	10.3390/diagnostics11101785
Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging	M. Beuque et al.	Computers in Biology and Medicine	04/10/2021	10.1016/j.compbiomed.2021.104918
A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis	C. Yan et al.	European Radiology	29/11/2021	10.1007/s00330-021-08365-z
A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images	M. Astaraki et al.	Frontiers in Oncology	17/12/2021	10.3389/fonc.2021.737368
Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis	Y. Liu et al.	Frontiers in Oncology	21/12/2021	10.3389/fonc.2021.801876
Bridging gaps between images and data: a systematic update on imaging biobanks	M. Gabelloni et al.	European Radiology	10/01/2022	10.1007/s00330-021-08431-6
A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses	M. Interlenghi, C. Salvatore et al.	Diagnostics	13/1/2022	10.3390/diagnostics12010187
Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients	C. Yan et al.	Annals of Translational Medicine	28/01/2022	10.21037/atm-21-4980
AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus	X. Zhou et al.	Neural Computing and Applications	24/02/2022	10.1007/s00521-022-07048-0
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis	X.Xing et al.	IEEE journal of biomedical and health informatics	09/03/2022	10.1109/JBHI.2022.3158897 https://spiral.imperial.ac.uk/handle/10044/1/95326
Automatic fine-grained glomerular lesion recognition in kidney pathology	Y. Nan et al.	Pattern Recognition	12/03/2022	10.1016/j.patcog.2022.108648 https://arxiv.org/abs/2203.05847
Swin transformer for fast MRI	Huang, J. et al.	Neurocomputing	12/04/2022	10.1016/j.neucom.2022.04.051
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers	K. Fatania et al.	2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)	26/04/2022	10.1109/ISBI52829.2022.9761603 https://arxiv.org/abs/2202.05269
Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper	L. Martí Bonmatí et al.	Insights into Imaging	10/05/2022	10.1186/s13244-022-01220-9
Automated detection and segmentation of non-small cell lung cancer computed tomography images	S. P. Primakov et al.	Nature Communications	14/06/2022	10.1038/s41467-022-30841-3
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels	M. Li et al.	IEEE Transactions on Emerging Topics in Computational Intelligence	21/07/2022	10.1109/TETCI.2022.3189054 https://spiral.imperial.ac.uk/handle/10044/1/97795
A federated cloud architecture for processing of cancer images on a distributed storage	J. Damián Segrelles Quilis et al.	Future Generation Computer Systems	22/09/2022	10.1016/j.future.2022.09.019
Deep learning based identification of bone scintigraphies containing metastatic bone disease foci	I. Abdallah et al.	Cancer Imaging	25/01/2023	10.1186/s40644-023-00524-3
Large-Kernel Attention for 3D Medical Image Segmentation	Li, H. et al.	Cognitive Computation	27/02/2023	10.1007/s12559-023-10126-7
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations	X. Xing et al.	IEEE Transactions on Medical Imaging	21/03/2023	10.1109/TMI.2023.3260169 https://spiral.imperial.ac.uk/handle/10044/1/103572
From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics	Z. Salahuddin et al.	Cancers	23/03/2023	10.3390/cancers15071932
Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects	H. Kondylakis et al.	European Radiology Experimental	08/05/2023	10.1186/s41747-023-00336-x
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation	Y. Nan et al.	IEEE Transactions on Neural Networks and Learning Systems	19/05/2023	10.1109/TNNLS.2023.3269223 https://arxiv.org/abs/2209.02048
Documenting the de-identification process of clinical and imaging data for AI for health imaging projects	Rocio Catalán et al.	Insights into Imaging	31/05/2024	https://doi.org/10.1186/s13244-024-01711-x
Analyzing the Total Segmentator for facial feature removal in head CT scans		Radiography	03/01/2025	10.1016/j.radi.2024.12.018
Prostate MRI: what to consider when shopping for AI tools		European Radiology	20/06/2024	https://doi.org/10.1007/s00330-024-10867-5