ArticlePLoS computational biology2023
Ten quick tips for computational analysis of medical images.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [Japanese journal of radiology · 2025Article
- Applying deep learning-based ensemble model to [Japanese journal of radiology · 2025Article
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- Article
- Machine learning approach usingJapanese journal of radiology · 2024Article
- Ten quick tips for electrocardiogram (ECG) signal processing.PeerJ. Computer science · 2024Article
- Application of Machine Learning Analyses Using Clinical and [Molecular imaging and biology · 2023Article
- Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.PLoS computational biology · 2023Article
- The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification.BioData mining · 2023Article
- Robust cardiac segmentation corrected with heuristics.PloS one · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.
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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.