Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index

The Anti-Nuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells in the Indirect Immuno-Fluorescence (IIF) assay protocol is considered the gold standard for detecting Connective Tissue Diseases. Computer-assisted systems for HEp-2 image analysis represent a growing field that harnesses the potential offered by novel machine learning techniques to address the classification of HEp-2 images and ANA patterns.

In this study, we introduce an innovative platform based on transfer learning with pre-trained deep learning models. This platform combines the power of unsupervised deep description of HEp-2 images, a novel feature selection approach designed for unbalanced datasets, and independent testing using two distinct datasets from different hospitals to tackle cross-hardware compatibility issues. To enhance the trustworthiness of our method, we also present a modified version of gradient-weighted class activation mapping for regional explainability and introduce a new sample quality index based on the Jensen-Shannon divergence to enhance method reliability and quantify sample heterogeneity.

The results we provide demonstrate exceptionally high performance in intensity and ANA pattern recognition when compared to state-of-the-art approaches. Our method's ability to eliminate the need for cell segmentation in favor of statistical analysis of the sample makes it applicable, robust, and versatile. Our future work will focus on addressing the challenge of mitotic spindle recognition by expanding our proposed approach to cover mixed patterns.