Mammography ML review: ROI extraction and classification comparison

Abstract-level methodological review of mammographic image classification. The authors describe comparison of ROI extraction strategies, machine-learning methods and datasets, with accuracy and frequency-based statistics, across a stated selection of 60 papers.

Workflow level n/a Source Peer-reviewed source Outcome Not empirically evaluated here

Evidence & verification

Source level

Peer-reviewed source
Grounded in a peer-reviewed publication. This describes the source; it does not mean the reported results have been independently reproduced.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Abstract supports ROI extraction, feature extraction/classification and comparison axes plus author-reported paper count. No assertion of best model, accuracy improvement, systematic-review rigor or clinical effectiveness is made.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Provides a bounded comparison structure for medical image-analysis research, not an autonomous review agent or clinical deployment recipe.

Getting started

Editorial suggestion, not executed: read the cited primary source, preserve its limitations and design a small researcher-supervised trial before reuse.

Maintenance

No maintenance or operational availability assurance.

License

Rights and licensing must be checked before reuse; not audited here.

Workflow & human oversight

Resource-level automation is unspecified. Researchers choose questions, evaluation design and decisions; documentation review is not workflow execution.

Workflow level is not an evidence score; higher is not better evidence.

Limitations

Abstract-only publisher review; selection criteria, datasets, per-model results and comparative superiority are not verified. The count of 60 papers is author-reported. Journal presents itself as peer-reviewed; review quality was not independently audited. This is not a clinically validated diagnostic tool.

Reproducibility

Source documentation reviewed only; no code, model outputs or scientific outcomes independently reproduced.

Adoption requirements

Editorial, untested guidance: consult the paper before designing an ROI classification comparison; independently define data splits, inclusion criteria and evaluation metrics. Clinical data require governance, consent/access authorization and specialist oversight.

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