Biomedical image analysis
Dotaz
Zobrazit nápovědu
elektronický časopis
sv.
- MeSH
- histologické techniky MeSH
- počítačové zpracování obrazu MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Buněčná biologie. Cytologie
- NLK Obory
- cytologie, klinická cytologie
- histologie
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.
- MeSH
- biomedicínské technologie klasifikace metody normy MeSH
- biomedicínský výzkum metody normy MeSH
- diagnostické zobrazování klasifikace metody normy MeSH
- hodnocení biomedicínských technologií metody normy MeSH
- lidé MeSH
- počítačové zpracování obrazu metody normy MeSH
- průzkumy a dotazníky MeSH
- reprodukovatelnost výsledků MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- MeSH
- algoritmy MeSH
- automatizované zpracování dat MeSH
- mikroskopie metody trendy MeSH
- počítačové zpracování obrazu normy MeSH
- software MeSH
- Publikační typ
- přehledy MeSH
... Data-base management program for biomedical devices 13 -- Csgries Zs.« Ko? ... ... Visualization» image understanding, and registration of 3-D brain images 23 -- Giakoumakis G F Panaviotakis ... ... A desk-top image analysis in an IBM PC environment 25 -- Groves P-M Linder ?-? ... ... —H- « -- Nieman H Integrated diagnosis by multi-modality imaging -- ? ... ... analysis Klochkov B.N. ...
vi, 115 stran ; 21 cm
The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit.
elektronický časopis
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
- MeSH
- algoritmy * MeSH
- počítačové zpracování obrazu * MeSH
- sémantika MeSH
- strojové učení MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Biosignal, ISSN 1211-412X vol. 19
1 CD ROM ; 13 cm
... Boissel 34 -- ADR Signal Analysis Project (ASAP), I.R. ... ... Hildebrand 148 -- Quantitative methods and texture analysis in magnetic resonance imaging (MRI) -tissue ... ... double resonance imaging (PEDRI), A. ... ... HUMAN GENOME ANALYSIS -- Introduction, M. ... ... Harris 702 -- Biomedical ethics in Europe: inventory, analysis, information, G. ...
Biomedical and health research, ISSN 0929-6743 vol. 9
xxxix, 744 s. ; 24 cm
