A cell-level discriminative neural network model for diagnosis of blood cancers
Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y
Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes.