# Image biomarker standardisation initiative

arXiv: https://arxiv.org/abs/1612.07003 · HTML: https://mlsanity.com/paper/1612.07003/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 30 of 63)

- [Class-Aware Adversarial Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2201.10737/) (2022)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data](https://mlsanity.com/paper/2101.11935/) (2021)
- [2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study](https://mlsanity.com/paper/2210.16640/) (2022)
- [Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0](https://mlsanity.com/paper/2412.10967/) (2024)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer](https://mlsanity.com/paper/2205.15290/) (2022)
- [Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines](https://mlsanity.com/paper/2207.14776/) (2022)
- [A Novel Collaborative Self-Supervised Learning Method for Radiomic Data](https://mlsanity.com/paper/2302.09807/) (2023)
- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging](https://mlsanity.com/paper/2407.13813/) (2024)
- [An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications](https://mlsanity.com/paper/2108.08618/) (2021)
- [Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling](https://mlsanity.com/paper/2203.04964/) (2022)
- [Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features](https://mlsanity.com/paper/2203.06314/) (2022)
- [Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)](https://mlsanity.com/paper/2405.01756/) (2024)
- [Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics](https://mlsanity.com/paper/2405.02334/) (2024)
- [Tumor Synthesis conditioned on Radiomics](https://mlsanity.com/paper/2509.24182/) (2025)
- [Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation](https://mlsanity.com/paper/2512.08216/) (2025)
- [Radiomics in Medical Imaging: Methods, Applications, and Challenges](https://mlsanity.com/paper/2602.00102/) (2026)
- [CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification](https://mlsanity.com/paper/2605.09002/) (2026)
- [Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis](https://mlsanity.com/paper/2202.13965/) (2022)
- [The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data](https://mlsanity.com/paper/2205.07516/) (2022)
- [Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens](https://mlsanity.com/paper/2208.13314/) (2022)
- [Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning](https://mlsanity.com/paper/2211.05241/) (2022)
- [Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning](https://mlsanity.com/paper/2302.08802/) (2023)
- [EGFR mutation prediction using F18-FDG PET-CT based radiomics features in non-small cell lung cancer](https://mlsanity.com/paper/2303.08569/) (2023)
- [Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapy](https://mlsanity.com/paper/2303.16123/) (2023)
- [Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations](https://mlsanity.com/paper/2306.00150/) (2023)

## 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.