ArticleScientific data2021
A DICOM dataset for evaluation of medical image de-identification.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- DCS-NET: a multi-task model for uterine ROI detection and automatic staging of early endometrial cancer in MRI.Scientific reports · 2025Article
- DICOM datasets for reproducible neuroimaging research across manufacturers and software versions.Scientific data · 2025Article
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.Journal of imaging informatics in medicine · 2025Article
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification.Journal of imaging informatics in medicine · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Video frame interpolation neural network for 3D tomography across different length scales.Nature communications · 2024Article
- A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.Scientific data · 2024Article
- Magnetic Resonance Imaging in Breast Cancer Tissue In Vitro after PDT Therapy.Diagnostics (Basel, Switzerland) · 2024Article
- Anonymizing Radiographs Using an Object Detection Deep Learning Algorithm.Radiology. Artificial intelligence · 2023Article
- Article
- Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.Nature communications · 2023Article
- Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction.Frontiers in neuroscience · 2023Article
- A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.Healthcare (Basel, Switzerland) · 2022Article
- Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design.Clinical pharmacology and therapeutics · 2022Review
- Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients.Frontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 1 country.
Funding
Abstract
We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.