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Keyword深度学习 有丝分裂细胞检测 生成对抗网络 多粒度级联森林方法 框回归过程 低秩表示
Other Abstract
Cancer is one of the major diseases with the highest incidence, which imposes serious threats to human health. Pathological cell image analysis is a key step in the diagnosis of cancer tumors, and is also recognized as the "gold standard". The counting of number of mitotic cells in histopathological images is one of the important indicators of cancer grading assessment. Due to the nature of the biological cell image, ordinary people cannot identify whether the cell is undergoing mitosis in the cancer tissue cell image, so a large number of experts are required to label the mitotic cell sample. However, the process of manually detecting and calculating mitotic cells is very time consuming, and the diagnosis results vary greatly among doctors. The severe deficiency of mitotic cell data has caused a non-negligible impact on the accuracy of automated detection of mitotic cells in computer-aided cancer pathology images. Therefore, there is an urgent need in the clinic for methods of automated detection and enumeration of mitotic cells.
In the second chapter, we first introduce several different forms of generative adversarial networks (GANs), and discuss in detail the research progress and application of these GANs in modeling, architecture, training and performance evaluation. Then, based on the appearance changes of the cell morphology of cancer cells, normal cells and mitotic cells, the CycleGAN was selected as a base method. Through unsupervised learning combined with loss of resistance and loss of circulation consistency, the mapping relationship between mitotic cell images and normal cell images, mitotic cell images, and cancerous cell images in breast cancer H&E-stained pathological images was obtained, and different cells were realized. The mutual conversion between type images generates more mitotic cell images. After verification by pathologists, these generated mitotic cell samples have high authenticity.
In Chapter 3, we present a region detection method based on multi-grainularity cascading forests (Regions with multi-Grained Cascade forest features, RgcForest), which applies the gcForest classification method to the cell mitosis detection task. First, the sliding window is added to the original algorithm, and then the image regions is fed and the results of “is mitosis cell or not” are produced. If it is a mitosis cell, the coordinates are also provided. Then, in order to reduce the unnecessary bounding box in the detection result, a non-maximal suppression algorithm is implemented on the gcForest algorithm. In the end, similar as the evolution from CNN to RCNN, the process of bounding box regression is implemented to further improve the localizatoin accuracy. The data generated by CycleGAN in the second chapter is used as the positive samples, and selects cancerous cells, normal cells, and cytoplasmic background images as negative samples. We train our gcForest-based method, and then uses the detection framework to effectively and accurately detection of mitotic cells.
In chapter 4, a mitotic detection method for breast cancer pathology image is proposed which is based on cascade low-rank multi-level decomposition representation. The non-mitotic part of different slices in the same patient is considered as a low-rank part in the matrix representations. Since mitotic cells have different shapes at different periods, and different nucleus in the same period have different shapes and textures during mitosis, they are regarded as sparse parts in the low rank decomposition. We adopt an iterative way to perform low-rank decomposition on the original data matrix, which not only reduces the rank of the new low-rank matrix gradually, but also avoid the problem of image representation capability degradation caused by unreasonable parameter selection. The model can be learned layer-by-layer. The number of Mitochondria candidates are removed as much as possible in the layer-by-layer matrix low-rank decomposition. For different levels of matrix decomposition representation, the hierarchical classifier corresponding to each layer is designed and trained. All the image patches containing mitotic cells and non-mitotic parts decomposed from each representation layer are fed into the corresponding layer-specific classifiers, and the mitotic cells in the pathological image of breast cancer can be accurately detected. Compared with existing methods, the proposed method can quickly and conveniently obtain the detection results, and does not require cell segmentation and additional model learning, providing a possibility for clinical application.
本文第三章提出了一个基于多粒度级联森林的特征区域检测方法,(Regions with multi-Grained Cascade forest features,RgcForest),将gcForest分类方法应用到乳腺肿瘤细胞图像的细胞有丝分裂检测问题,借助gcForest良好的特征提取和分类性能,通过候选区域方法(Region Proposal)实现目标检测问题的转化。首先在原始算法上增加滑动窗口功能,然后把剪切好的图像给输入进去,并判断是否有丝分裂细胞,如果是则给出坐标。然后,为了减少检测结果中不必要的边框(bounding box),在gcForest算法上实现了非极大值抑制算法。最后,仿照CNN到RCNN的演进,增加边框坐标回归(bounding box regression)过程,进一步提高定位效果。实验选择第二章用CycleGAN模型生成的有丝分裂样本作为正样本,选择癌变细胞、正常细胞和细胞质背景图像作为负样本,在原始的gcForest框架上训练输出图像的概率值,再利用检测框架有效准确地检测有丝分裂细胞。
Document Type研究报告
Recommended Citation
GB/T 7714
杨晨雪. 基于深度多层表示的乳腺癌病理图像细胞有丝分裂检测方法. 2018.
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