MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning

Abstract

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named ''MitosisNet''. MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region detection and the classification model is applied for further confirmation of the mitosis regions. In addition, an integrated multi-patch reference scheme and a novel confidence analysis strategy are introduced for improving overall detection performance during testing. The proposed system is evaluated on three different publicly available datasets including MITOSIS 2012, MITOSIS 2014, and Case Western Reserve University (CWRU) datasets. The experimental results demonstrate state-of-the-art performance compared to the existing methods and the proposed approach is fast enough in order to meet the requirements of clinical practice.

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

“…Therefore, Li et al (2018) exploited the region information of the mitosis using VGG16 backboned faster R-CNN to filter out the probable mitotic regions that are further refined by assigning the predictions to another deep network to remove false positives 32 . Likewise, MitosisNet proposed by Alom et al (2020) also consisted of multiple deep learning models, including segmentation, detection, and classification models for the final decision of the mitosis regions 33 . Similarly, Mehmood et al handle the complex nature of mitosis by initially identifying probable mitotic regions through R-CNN.…”

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic

“…Table 3 lists the CNN models used for COVID-19 image segmentation. Alom et al [ 57 ] used an Inception Residual Recurrent CNN (IRRCNN) with a transfer learning approach for the detection of COVID-19. The model was evaluated for both chest X-ray and CT images, and a quantitative evaluation was performed to determine the severity of COVID-19.…”

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

“…DeepMitosis scheme functioned in multi-stage pipeline that made it relatively slow and inelegant for clinical applications. Zahangir et al [8] perform the mitosis detection using 'MitosisNet' which consists of three different models i.e. segmentation, detection, and classification modules.…”

“…segmentation, detection, and classification modules. In [8] Region of Interest ROIs are extracted by applying segmentation and detection modules while the mitosis and non-mitosis regions are categorized by classification model. Mahmood et al [9] present a multistage mitotic-cell detection method.…”