| Title |
A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections
|
|---|---|
| Published in |
PLOS ONE, May 2013
|
| DOI | 10.1371/journal.pone.0062070 |
| Pubmed ID | |
| Authors |
Elton Rexhepaj, Margrét Agnarsdóttir, Julia Bergman, Per-Henrik Edqvist, Michael Bergqvist, Mathias Uhlén, William M. Gallagher, Emma Lundberg, Fredrik Ponten |
| Abstract |
Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Spain | 1 | 20% |
| Australia | 1 | 20% |
| Canada | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Netherlands | 1 | 1% |
| Germany | 1 | 1% |
| Singapore | 1 | 1% |
| Czechia | 1 | 1% |
| Unknown | 70 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 25% |
| Student > Master | 12 | 16% |
| Student > Ph. D. Student | 12 | 16% |
| Student > Postgraduate | 8 | 11% |
| Student > Bachelor | 5 | 7% |
| Other | 11 | 14% |
| Unknown | 9 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 28% |
| Computer Science | 12 | 16% |
| Agricultural and Biological Sciences | 9 | 12% |
| Engineering | 9 | 12% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Other | 7 | 9% |
| Unknown | 13 | 17% |