Name
Abstract | ||
|---|---|---|
Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32251-9_39 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11767 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zach Eaton-Rosen | 1 | 65 | 7.69 |
| Thomas Varsavsky | 2 | 1 | 1.42 |
| Sébastien Ourselin | 3 | 2499 | 237.61 |
| Cardoso M. Jorge | 4 | 64 | 13.70 |