Mi-DETR: For Mitosis Detection From Breast Histopathology Images an Improved DETR

In histopathological image analysis, the detection and count of mitotic cells are important biomarkers for determining the degree and aggressiveness of cancer prognosis. Manual detection of mitosis by pathologists is a lengthy and challenging process. With advancements in deep learning architectures, numerous automatic mitotic detection methods have been proposed. However, most mitotic detection methods lack generalizability across image areas and are not consistently reproducible in multi-center environments. To overcome these issues, a new automatic mitotic detection approach called the Mi-DETR, based on the DETR architecture, has been proposed. In the proposed Mi-DETR model, the backbone of the original DETR is replaced by CSPResNeXt. The aim of this is to strengthen the learning capacity in feature extraction and increase the variability of the learned features. In this way, information loss and unwanted gradient flow are avoided. In the decoder layer, unnecessary model parameters have been filtered out using a layer reduction strategy to improve model efficiency and reduce computational costs. Additionally, a more stable model has been obtained by using the CIoU loss function instead of the L1+GIoU loss function used in the DETR model. The publicly available ICPR14 and TUPAC16 breast histopathology datasets were used for training, validation, and testing in the experiments. The results provided more precise and compact bounding boxes close to clinically validated ground truth, demonstrating the accuracy and generalizability of the proposed model. As a result, the proposed Mi-DETR model achieved a 0.921 F1-score on the ICPR14 dataset and a 0.950 F1-score on the TUPAC16 dataset. The results obtained on both datasets demonstrate that the proposed model performs well enough to compete with state-of-the-art deep learning architectures.