论文标题
有丝分裂计数的深度特征融合
Deep Feature Fusion for Mitosis Counting
论文作者
论文摘要
每个居住在美国的妇女都有大约八分之一的侵入性乳腺癌的机会。有丝分裂细胞计数是评估乳腺癌侵略性或等级的最常见测试之一。在这种预后，必须使用高分辨率显微镜来计算细胞的病理学家检查组织病理学图像。不幸的是，这可能是一项详尽的任务，重现性差，尤其是对于非专家而言。深度学习网络最近已适应能够自动定位这些感兴趣区域的医疗应用程序。但是，这些基于区域的网络缺乏利用完整图像CNN产生的分割特征的能力，该特征通常被用作唯一的检测方法。因此，所提出的方法利用RCNN更快地进行对象检测，同时融合了具有RGB图像特征的UNET生成的分割特征，以在Mitos-Atypia 2014 2014有丝分裂计数挑战数据集上实现0.508的F评分，均超过了最好的State-Art-Art方法。
Each woman living in the United States has about 1 in 8 chance of developing invasive breast cancer. The mitotic cell count is one of the most common tests to assess the aggressiveness or grade of breast cancer. In this prognosis, histopathology images must be examined by a pathologist using high-resolution microscopes to count the cells. Unfortunately, this can be an exhaustive task with poor reproducibility, especially for non-experts. Deep learning networks have recently been adapted to medical applications which are able to automatically localize these regions of interest. However, these region-based networks lack the ability to take advantage of the segmentation features produced by a full image CNN which are often used as a sole method of detection. Therefore, the 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, outperforming state-of-the-art methods.
