Dynamic feature extraction and histopathology domain shift alignment for mitosis detection

Abstract

Mitosis count is of crucial significance in cancer diagnosis; therefore, mitosis detection is a meaningful subject in medical image studies. The challenge of mitosis detection lies in the intra-class variance of mitosis and hard negatives, i.e., the sizes/ shapes of mitotic cells vary considerably and plenty of non-mitotic cells resemble mitosis, and the histopathology domain shift across datasets caused by different tissues and organs, scanners, labs, etc. In this paper, we propose a novel Domain Generalized Dynamic Mitosis Detector (DGDMD) to handle the intra-class variance and histopathology domain shift of mitosis detection with a dynamic mitosis feature extractor based on residual structured depth-wise convolution and domain shift alignment terms. The proposed dynamic mitosis feature extractor handles the intra-class variance caused by different sizes and shapes of mitotic cells as well as non-mitotic hard negatives. The proposed domain generalization schedule implemented via novel histopathology-mitosis domain shift alignments deals with the domain shift between histopathology slides in training and test datasets from different sources. We validate the domain generalization ability for mitosis detection of our algorithm on the MIDOG++ dataset and typical mitosis datasets, including the MIDOG 2021, ICPR MITOSIS 2014, AMIDA 2013, and TUPAC 16. Experimental results show that we achieve state-of-the-art (SOTA) performance on the MIDOG++ dataset for the domain generalization across tissue and organs of mitosis detection, across scanners on the MIDOG 2021 dataset, and across data sources on external datasets, demonstrating the effectiveness of our proposed method on the domain generalization of mitosis detection.