Explainable Artificial Intelligence Approach Using Low-Dimensional Visualization and Ensembling Uncertainty Quantification for Rare Chromosomal Aberration Detection in Cytogenetic Imaging

Motivation: In biological dosimetry a radiation dose is estimated using the average number of chromosomal aberrations per peripheral blood lymphocytes. This analysis is manually performed and better automating chromosomal aberration detection has become of paramount of importance to improve diagnosis reliability and reduce human expertise time. Results: We build an object detection model to automate chromosomal aberration detection using deep convolutional neural networks. We formulated the problem of rare aberration detection in metaphase images as a heatmap regression problem. We demonstrate significant performance improvements and provide statistical confidence intervals by using an ensemble of checkpoints that are selected at the end of each epoch during training and combined to generate a consensus model. We also propose in this work an approach to visualize training dynamics using low-dimension representation to better interpret the relationships between training stochasticity and ensemble diversity. This approach provided an conveniant explainable artificial intelligence tool to understand the mechanism of the chromosomal aberration detection of the model.