A refinement on detection in cell counting

Cell counting is an important task and it's difficult for the naked eyes to keep counting cells one by one for a long time. In this paper, we use multiple random forests (RFs) to detect cells and propose a post-processing to refine the detection result. Each RF generates a different detection r...

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Bibliographic Details
Published in2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) pp. 306 - 309
Main Authors Jiang, Ni, Yu, Feihong
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.01.2021
Subjects
cell counting
cell detection
Conferences
Consumer electronics
Forestry
Radio frequency
random forest
Reliability
Task analysis
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DOI10.1109/ICCECE51280.2021.9342387

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Summary:Cell counting is an important task and it's difficult for the naked eyes to keep counting cells one by one for a long time. In this paper, we use multiple random forests (RFs) to detect cells and propose a post-processing to refine the detection result. Each RF generates a different detection result and averaging all detection results can decrease the count error. For some false detected cells approved by a few RFs, they have low confidence in the averaged detection result. To improve the count accuracy further, we propose a post-processing to remove the low-confident detections and enhance the high-confident detections. We validate our proposed method on two cell datasets and prove that our proposed method can refine the count result and improve the performance.
DOI:10.1109/ICCECE51280.2021.9342387