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Computer assisted tools can provide cost effective and easily deployable solutions for cancer diagnostics. The aim of this challenge is to build a classifier for the identification of leukemic versus normal immature cells for while blood cancer, namely, B-ALL diagnostics. A dataset of cells with class labels, marked by the expert based on the domain knowledge, will be provided at the subject-level to train the classifier. This problem is interesting because the two cell types appear similar under the microscope and subject-level variability plays a key role. Hence, it is challenging to build a classifier that can yield good results on prospective data.
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