ArticleEuropean radiology experimental2020
Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
Article in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- A proposed radiological model for preoperative chemotherapy response prediction in patients with skeletal Ewing sarcoma.Skeletal radiology · 2026Article
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- The evaluation of artificial intelligence in mammography-based breast cancer screening: Is breast-level analysis enough?European radiology · 2025Article
- Deep learning model for assessing survival benefits in hepatocellular carcinoma patients undergoing intra-arterial therapies based on proliferative subtype.NPJ digital medicine · 2025Article
- EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning.NPJ digital medicine · 2025Article
- Federated learning as a smart tool for research on infectious diseases.BMC infectious diseases · 2024Review
- Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology.Insights into imaging · 2024Article
- Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.European radiology experimental · 2024Article
- Saliency of breast lesions in breast cancer detection using artificial intelligence.Scientific reports · 2023Article
- Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.European heart journal · 2023Article
- Open issues for education in radiological research: data integrity, study reproducibility, peer-review, levels of evidence, and cross-fertilization with data scientists.La Radiologia medica · 2023Article
- Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation.NPJ digital medicine · 2022Article
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.The journal of pathology. Clinical research · 2022Article
- Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.European radiology experimental · 2021Review
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.
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