Combining radiology images and clinical metadata for multimodal medical case-based retrieval

Jimenez-del-Toro, Oscar (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Cirujeda, Pol (Department of Information and Communication TechnologiesUniversitat Pompeu Fabra, Barcelona, Spain) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University Hospitals of Geneva, Switzerland)

As part of their daily workload, clinicians examine patient cases in the process of formulating a diagnosis. These large multimodal patient datasets stored in hospitals could help in retrieving relevant information for a differential diagnosis, but these are currently not fully exploited. The VISCERAL Retrieval Benchmark organized a medical case-based retrieval algorithm evaluation using multimodal (text and visual) data from radiology reports. The common dataset contained patient CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans and RadLex term anatomy–pathology lists from the radiology reports. A content-based retrieval method for medical cases that uses both textual and visual features is presented. It defines a weighting scheme that combines the anatomical and clinical correlations of the RadLex terms with local texture features obtained from the region of interest in the query cases. The visual features are computed using a 3D Riesz wavelet texture analysis performed on a common spatial domain to compare the images in the analogous anatomical regions of interest in the dataset images. The proposed method obtained the best mean average precision in 6 out of 10 topics and the highest number of relevant cases retrieved in the benchmark. Obtaining robust results for various pathologies, it could further be developed to perform medical case-based retrieval on large multimodal clinical datasets.


Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Economie/gestion
Adresse bibliogr.:
Cham, Springer
Date:
Cham
Springer
2017
Pagination:
pp. 221-236
Veröffentlicht in:
Cloud-based benchmarking of medical image analysis
DOI:
ISBN:
978-3-319-49642-9
Ressource(s) externe(s):
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 Datensatz erzeugt am 2017-10-23, letzte Änderung am 2018-12-20

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