Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling (TS), a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that TS is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement.

Automated summary

In this study, state-of-the-art Convolutional Neural Networks are used to classify immunofluorescence in renal biopsy, with a focus on addressing the issue of overconfident outputs. The research demonstrates the successful application of Temperature Scaling (TS) for providing reliable probabilities in this context, showing good accuracy and reliability in a task with low inter-rater agreement.