Experiment Overview
| Repository ID: | FR-FCM-Z2JW | Experiment name: | Hematologist-level classification of mature B-cell neoplasm using deep learning on multiparameter flow cytometry data | MIFlowCyt score: | 31.50% |
| Primary researcher: | Max Zhao | PI/manager: | Peter Krawitz | Uploaded by: | Max Zhao |
| Experiment dates: | 2016-01-01 - 2018-12-31 | Dataset uploaded: | Apr 2020 | Last updated: | Oct 2020 |
| Keywords: | [multicolor flow cytometry] [Deep Learning] [neural networks] [Non-Hodgkin Lymphoma] | Manuscripts: | |||
| Organizations: |
Münchner Leukämielabor (MLL) GmbH, München, (Germany)
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| Purpose: | Classify B-NHL subtypes from blood and bone marrow samples | ||||
| Conclusion: | We developed an automated model to directly classify FCS data into multiclass diagnosis label without the need for human supervision or any manual gating. Our model achieved a weighted F1-score of 0.94 for an eight-class classification: CLL/MBL, PL, FL, HCL, LPL, MCL, MZL and healthy controls. | ||||
| Comments: | None | ||||
| Funding: | Not disclosed | ||||
| Quality control: | Navios cytometer was calibrated according to the manufacturer’s recommendations | ||||
