| Popis: |
Mitotic figure count acts as a proliferative marker to measure aggressiveness of the breast cancer tumor. In this article, we have proposed a novel framework named SmallMitosis to detect mitotic cells particularly very small size mitosis from hematoxylin and eosin (H&E) stained breast histology images. SmallMitosis framework consists of an atrous fully convolution based segmentation (A-FCN) model and a deep multiscale (MS-RCNN) detector. In intended A-FCN model, the concept of atrous convolution helps to estimate mitosis mask and bounding box annotations of very small size mitotic cells. Meanwhile, the architecture of MS-RCNN internally lifts poor representations of small mitosis to “super-resolved” ones, that are similar to real large mitosis thus more discriminative for detection of small size blurred mitotic cells, as well as a fully convolution layer at detection stage, decreases computational cost. The A-FCN model trained on fully labeled mitosis datasets (all pixels of mitosis are labeled) is applied on weakly labeled datasets (only centroid pixel is labeled) to obtain mitosis mask and bounding box annotations. Using these estimated bounding box annotations, MS-RCNN detector is trained to detect small size mitosis from weakly labeled datasets. The performance of the proposed scheme is tested on three publicly available mitosis datasets, namely ICPR 2012, ICPR 2014, and AMIDA13. On challenging ICPR 2012 dataset, we obtained F score of 0.902, outperforming all prior detection systems significantly. On ICPR 2014 and AMIDA13 datasets, we achieved a 0.495 and 0.644 F score respectively. The results demonstrated that our method impressively outperforms state-of-the-art approaches. SmallMitosis is available at https://github.com/tasleem-hello/SmallMitosis. |
