ERDS: Optimizing Domains Using Observation-Based Extremity

Medical disease diagnosis relies on effective labeling and segmentation of cells in biopsy slides. Current deep learning approaches have shown strong results in addressing many of the traditional issues with medical image segmentation. However, none of these approaches address a medical problem by using observation-specific training data to drive further research. A new method named Extremity-Ranked Domain Selection is created by creating an extremity metric, ranking patients based on their extremity, and evaluating a multisource domain adversarial network (MDAN) approach using a full factorial design of experiments. ERDS yields the optimal values in the full factorial experiment of a domain size of 15, domain choice of Extremity Ranked, and a classification threshold of 0.7. Additionally, ERDS provides comparative performance in relation to a standard Monte Carlo Dropout UNet model with multiple factor combinations outperforming (Patients E-139: 0.65, E-247: 0.627, E-147: 0.624, E-92: 0.623) this baseline model (0.571). This work illustrates the impact of extremity metric in future works and drives further research into effectively modifying training data to optimize model performance.