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Image biomarker standardisation initiative

arXiv 1612.07003 ↗First posted 21 Dec 2016cs.CV eess.IVcited by 61

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Abstract (arXiv)

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2016.

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Citing papers per year: 2021: 5; 2022: 10; 2023: 8; 2024: 8; 2025: 11; 2026: 19.

61 papers.

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  1. 2022Class-Aware Adversarial Transformers for Medical Image Segmentationcited by 33
  2. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25
  3. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  4. 2021FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imagingcited by 13
  5. 2021A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Datacited by 3
  6. 20222D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Studycited by 3
  7. 2024Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0cited by 3
  8. 2021AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomicscited by 2
  9. 2022Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformercited by 2
  10. 2022Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelinescited by 2
  11. 2023A Novel Collaborative Self-Supervised Learning Method for Radiomic Datacited by 2
  12. 2023Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Modelscited by 2
38 more
  1. 2024A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimagingcited by 2
  2. 2021An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applicationscited by 1
  3. 2022Metastatic Cancer Outcome Prediction with Injective Multiple Instance Poolingcited by 1
  4. 2022Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Featurescited by 1
  5. 2024Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)cited by 1
  6. 2024Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomicscited by 1
  7. 2025Tumor Synthesis conditioned on Radiomicscited by 1
  8. 2025Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentationcited by 1
  9. 2026Radiomics in Medical Imaging: Methods, Applications, and Challengescited by 1
  10. 2026CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classificationcited by 1
  11. 2022Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysiscited by 0
  12. 2022The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open datacited by 0
  13. 2022Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimenscited by 0
  14. 2022Reproducibility in medical image radiomic studies: contribution of dynamic histogram binningcited by 0
  15. 2023Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learningcited by 0
  16. 2023EGFR mutation prediction using F18-FDG PET-CT based radiomics features in non-small cell lung cancercited by 0
  17. 2023Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapycited by 0
  18. 2023Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotationscited by 0
  19. 2023Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumescited by 0
  20. 2023Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma Chemoradiotherapy using Planning CT-based Radiomics Modelcited by 0
  21. 2024Enhancing predictive imaging biomarker discovery through treatment effect analysiscited by 0
  22. 2024MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosiscited by 0
  23. 2024Uncertainty quantification for improving radiomic-based models in radiation pneumonitis predictioncited by 0
  24. 2025Multi-Omics Fusion with Soft Labeling for Enhanced Prediction of Distant Metastasis in Nasopharyngeal Carcinoma Patients after Radiotherapycited by 0
  25. 2025Glioma Classification using Multi-sequence MRI and Novel Wavelets-based Feature Fusioncited by 0
  26. 2025An Explainable Neural Radiomic Sequence Model with Spatiotemporal Continuity for Quantifying 4DCT-based Pulmonary Ventilationcited by 0
  27. 2025Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imagingcited by 0
  28. 2025Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0cited by 0
  29. 2025Consensus in the Parliament of AI: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures for Multicentre NSCLC Risk Stratificationcited by 0
  30. 2025Random forest-based out-of-distribution detection for robust lung cancer segmentationcited by 0
  31. 2025Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkerscited by 0
  32. 2025LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysiscited by 0
  33. 2026PSSF: Early osteoarthritis detection using physical synthetic knee X-ray scans and AI radiomics modelscited by 0
  34. 2026Pretty Good Measurement for Radiomics: A Quantum-Inspired Multi-Class Classifier for Lung Cancer Subtyping and Prostate Cancer Risk Stratificationcited by 0
  35. 2026SA-CycleGAN-2.5D: Self-Attention CycleGAN with Tri-Planar Context for Multi-Site MRI Harmonizationcited by 0
  36. 2026RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstructioncited by 0
  37. 2026Radiuma: A Unified Zero-Code Executable Graphical Workflow Generator for Reproducible and Shareable Medical Image Analysis and Machine Learningcited by 0
  38. 2026Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasetscited by 0

50 most cited of 61 All 61 (JSON)