Abstract
Acute lymphoblastic leukemia (ALL) is a blood cancer that leads to 111,000 death globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle the above challenges. The weighted \(F_1\)-scores of the preliminary test set and final test are 0.84 and 0.88, respectively employing our ensemble model predictions and ranked within the top 10% in ISBI-2019 Classification of Normal versus Malignant White Blood Cancer Cells contest. Our results preliminarily demonstrate the efficacy of employing DL based techniques in ALL cells image analysis.
The pre-print is online: Y. Liu and F. Long, Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning, bioRxiv, 2019, doi: https://doi.org/10.1101/580852.
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Liu, Y., Long, F. (2019). Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_12
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