A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch

• URL: http://arxiv.org/abs/2301.07627v1
• Date: Wed, 18 Jan 2023 16:11:09 GMT
• Title: A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
• Authors: Huadeng Wang, Hao Xu, Bingbing Li, Xipeng Pan, Lingqi Zeng, Rushi Lan, Xiaonan Luo
• Abstract summary: We propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible. In the second stage, a classification network M_class is proposed to refine the results of the first stage.
• Score: 12.701748529240183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-consuming. In this paper, we propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible. In the second stage, a classification network M_class is proposed to refine the results of the first stage. In addition, the attention mechanism, normalization method, and hybrid anchor branch classification subnet are introduced to improve the overall detection performance. Our method achieves the current highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated our method on the GZMH dataset released by our research team for the first time and reached the highest F1-score of 0.563, which is also better than multiple classic detection networks widely used at present. It confirmed the effectiveness and generalization of our method. The code will be available at: https://github.com/antifen/mitosis-nuclei-detection.

Related papers

• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification [10.960222475663006]
  Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. We propose a two-stage mitosis segmentation and classification method, named SCMitosis. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained.
  arXiv  Detail & Related papers  (2022-12-27T08:12:42Z)
• ReCasNet: Improving consistency within the two-stage mitosis detection framework [5.263015177621435]
  Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. This pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions. We propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements.
  arXiv  Detail & Related papers  (2022-02-28T16:03:14Z)
• PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification [23.466331358975044]
  We study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin stained histopathology data. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types.
  arXiv  Detail & Related papers  (2021-11-01T08:29:40Z)
• Deep neural networks approach to microbial colony detection -- a comparative analysis [52.77024349608834]
  This study investigates the performance of three deep learning approaches for object detection on the AGAR dataset. The achieved results may serve as a benchmark for future experiments.
  arXiv  Detail & Related papers  (2021-08-23T12:06:00Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification [6.85316573653194]
  We propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification.
  arXiv  Detail & Related papers  (2020-10-30T20:03:20Z)
• Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images [51.893494939675314]
  We propose a novel weakly supervised segmentation framework based on partial points annotation. We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-10T15:41:29Z)
• Deep Feature Fusion for Mitosis Counting [0.0]
  The mitotic cell count is one of the most common tests to assess the aggressiveness or grade of breast cancer. Deep learning networks have been adapted to medical applications which are able to automatically localize regions of interest. A proposed method leverages Faster RCNN for object detection while fusing segmentation features generated by a UNet with RGB image features to achieve an F-score of 0.508 on the MITOS-ATYPIA 2014 mitosis counting challenge dataset.
  arXiv  Detail & Related papers  (2020-02-01T20:20:00Z)
• MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
  Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. The use of gradient combined nonvolutionity renders learning susceptible to novel problems. We propose fusing neighboring layers of deeper networks that are trained with random variables.
  arXiv  Detail & Related papers  (2020-01-28T18:25:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.