Challenging mitosis detection algorithms: Global labels allow centroid localization
This work addresses the challenge of low reproducibility in mitosis counting for pathologists by simplifying the labeling process, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of mitosis detection in cancer diagnosis by proposing a weakly supervised method using only image-level labels, achieving an F1-score of 0.729 on the TUPAC16 dataset, which is competitive with state-of-the-art methods that require more complex, multi-stage approaches.
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern heterogeneity. To improve reproducibility, deep learning methods have been proposed in the last years using convolutional neural networks. However, these methods have been hindered by the process of data labelling, which usually solely consist of the mitosis centroids. Therefore, current literature proposes complex algorithms with multiple stages to refine the labels at pixel level, and to reduce the number of false positives. In this work, we propose to avoid complex scenarios, and we perform the localization task in a weakly supervised manner, using only image-level labels on patches. The results obtained on the publicly available TUPAC16 dataset are competitive with state-of-the-art methods, using only one training phase. Our method achieves an F1-score of 0.729 and challenges the efficiency of previous methods, which required multiple stages and strong mitosis location information.