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Deep learning models for clinical diagnosis, prognosis and treatment need to be trustworthy and robust for clinical deployment, given that model predictions often directly inform a subsequent course of action, where individual patient lives are at stake. Central to model robustness is repeatability, or the ability of a model to generate near-identical predictions under identical conditions. In this work, we optimize focal loss as a cost function to improve repeatability of model predictions on two clinically significant classification tasks: knee osteoarthritis grading and breast density classification, with and without the presence of Monte Carlo (MC) Dropout. We discover that in all experimental instances, focal loss improves repeatability of the resulting models, an effect compounded in the presence of MC Dropout.
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Friday 8th July
Poster Session 3.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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