학술논문
Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification
Document Type
Conference
Author
Source
2020 25th International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2020 25th International Conference on. :1298-1305 Jan, 2021
Subject
Language
Abstract
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.