| Title |
Mining biomedical images towards valuable information retrieval in biomedical and life sciences
|
|---|---|
| Published in |
Database: The Journal of Biological Databases & Curation, August 2016
|
| DOI | 10.1093/database/baw118 |
| Pubmed ID | |
| Authors | |
| Abstract |
Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Switzerland | 1 | 20% |
| Spain | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 40% |
| Scientists | 2 | 40% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 21% |
| Researcher | 8 | 21% |
| Student > Bachelor | 6 | 16% |
| Lecturer | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 4 | 11% |
| Unknown | 6 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 16% |
| Engineering | 6 | 16% |
| Medicine and Dentistry | 5 | 13% |
| Agricultural and Biological Sciences | 4 | 11% |
| Computer Science | 4 | 11% |
| Other | 5 | 13% |
| Unknown | 8 | 21% |