Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning
文献类型：期刊论文
| 作者 | Liu, Hong1; Xu, Wen-Dong1,2; Shang, Zi-Hao1,2; Wang, Xiang-Dong1; Zhou, Hai-Yan3; Ma, Ke-Wen3; Zhou, Huan3; Qi, Jia-Lin3; Jiang, Jia-Rui3; Tan, Li-Lan3 |
| 刊名 | FRONTIERS IN ONCOLOGY
 |
| 出版日期 | 2022-04-14 |
| 卷号 | 12页码:11 |
| 关键词 | pathological image weakly supervised learning molecular subtype breast cancer H&E |
| ISSN号 | 2234-943X |
| DOI | 10.3389/fonc.2022.858453 |
| 英文摘要 | Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic. |
| 资助项目 | Beijing Natural Science Foundation[Z190020] ; National Natural Science Foundation of China[81972490] ; Natural Science Foundation of Hunan Province[2019JJ50781] |
| WOS研究方向 | Oncology |
| 语种 | 英语 |
| WOS记录号 | WOS:000795567500001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19540]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Hong; Wang, Kuan-Song |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Cent South Univ, Xiangya Hosp, Dept Pathol, Changsha, Peoples R China 4.Cent South Univ, Sch Basic Med Sci, Changsha, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Hong,Xu, Wen-Dong,Shang, Zi-Hao,et al. Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning[J]. FRONTIERS IN ONCOLOGY,2022,12:11. |
| APA | Liu, Hong.,Xu, Wen-Dong.,Shang, Zi-Hao.,Wang, Xiang-Dong.,Zhou, Hai-Yan.,...&Qian, Yue-Liang.(2022).Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning.FRONTIERS IN ONCOLOGY,12,11. |
| MLA | Liu, Hong,et al."Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning".FRONTIERS IN ONCOLOGY 12(2022):11. |
入库方式： OAI收割
来源：计算技术研究所
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