Name
Abstract | ||
|---|---|---|
The ability to accurately quantitate specific populations of cells is important for precision diagnostics in laboratory medicine. For example, the quantization of positive tumor cells can be used clinically to determine the need for chemotherapy in a cancer patient. In this paper, we describe a supervised learning framework with Convolutional Neural Network (CNN) and cast the cell counting task as a regression problem, where the global cell count is taken as the annotation to supervise training, instead of following the classification or detection framework. To further decrease the prediction error of counting, we tune several cutting-edge CNN architectures (e.g. Deep Residual Network) into the regression model. As the final output, not only the cell count is estimated for an image, but also its spatial density map is provided. The proposed method is evaluated with three state-of-the-art approaches on three cell image datasets and obtain superior performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46604-0_20 | ECCV Workshops |
Keywords | Field | DocType |
Cell counting,Convolution neural network,Deep residual net,Detection,Classification | Residual,Mean squared prediction error,Pattern recognition,Regression,Regression analysis,Convolutional neural network,Computer science,Supervised learning,Artificial intelligence,Cell counting,Quantization (signal processing),Machine learning | Conference |
Citations | PageRank | References |
9 | 0.89 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Xue | 1 | 79 | 6.56 |
| Ray Nilanjan | 2 | 541 | 55.39 |
| Judith Hugh | 3 | 11 | 1.62 |
| Gilbert Bigras | 4 | 11 | 1.96 |