Abstract
This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powerful object detection methods hardly be used in mitosis detection. Aiming at solving this problem, this paper firstly applies a CNN-based algorithm to pixelwisely segment the mitosis regions, based on which bounding boxes of mitosis are generated as strong labels. Based on the generated bounding boxes, an object detection network is trained to accomplish mitosis detection. Experimental results show that the proposed method is effective in detecting mitosis, and the accuracies outperform state-of-the-art literatures.
Highlights
Breast cancer is one of the main threats to woman health and becomes one of the most leading causes of cancer-related death all over the world
This paper proposes to generate bounding boxes for weakly labeled breast histopathology images and construct a mitosis detection method based on object detection networks
To further corroborate the effectiveness of the label generation strategy used in this paper, an additional experiment is performed on the TUPAC2016 dataset, and the results are listed in Table 2, in which Manual means the bounding boxes of mitosis are marked manually, i.e., the minimum rectangle that can surround the mitosis and include the ground truth point, U-Net means the mitosis is segmented using the original U-Net network [25], and Proposed means the mitosis is segmented using the proposed segmentation network
Summary
Breast cancer is one of the main threats to woman health and becomes one of the most leading causes of cancer-related death all over the world. Diagnosis is believed to be an effective way for promoting the prognosis of breast cancer. Breast cancer can be classified into three levels in histopathology based on the morphological microstructure of cancerous and the normal cells, i.e., well differentiated, poorly differentiated, and intermediate. Classification is important to the diagnosis and prognosis of breast cancer. The most commonly used classification standard is the BRE system proposed by WHO, in which three indications are used to evaluate the differentiation level. The indications are vasculogenesis degree, nuclear atypia, and mitotic counting
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
