Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning | |
Fang, Mengjie1,2; Zhang, Wenjuan3; Dong, Di1,2; Zhou, Junlin3; Tian, Jie1,2 | |
2019 | |
会议名称 | SPIE Medical Imaging 2019 |
会议日期 | 2019-2 |
会议地点 | San Diego, California, USA |
摘要 | In this paper, we investigate the problem of predicting the histopathological findings of gastric cancer (GC) from preoperative CT image. Unlike most existing classification systems assess the global imaging phenotype of tissues directly, we formulate the problem as a generalized multi-instance learning (GMIL) task and design a deep GMIL framework to address it. Specifically, the proposed framework aims at training a powerful convolutional neural network (CNN) which is able to discriminate the informative patches from the neighbor confusing patches and yield accurate patient-level classification. To achieve this, we firstly train a CNN for coarse patch-level classification in a GMIL manner to develop several groups which contain the informative patches for each histopathological category, the intra-tumor ambiguous patches, and the extra-tumor irrelative patches respectively. Then we modify the fully-connected layer to introduce the latter two classes of patches and retrain the CNN model. In the inference stage, patient-level classification is implemented based on the group of candidate informative patches automatically recognized by the model. To evaluate the performance and generalizability of our approach, we successively apply it to predict two kinds of histopathological findings (differentiation degree [two categories] and Lauren classification [three categories]) on a dataset including 433 GC patients with venous phase contrast-enhanced CT scans. Experimental results reveal that our deep GMIL model has a powerful predictive ability with accuracies of 0.815 and 0.731 in the two applications respectively, and it significantly outperforms the standard CNN model and the traditional texture-based model (more than 14% and 17% accuracy increase). |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48531 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Tian, Jie |
作者单位 | 1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, Gansu, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fang, Mengjie,Zhang, Wenjuan,Dong, Di,et al. Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning[C],2019. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Predicting histopath(500KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论