Knowledge Commons of Institute of Automation,CAS
EffiDiag: an Efficient Framework for Breast Cancer Diagnosis in Multi-Gigapixel Whole Slide Images | |
Shuyan Liu1,2![]() ![]() | |
2020 | |
会议名称 | 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
页码 | 663-669 |
会议日期 | 2020-12-16 |
会议地点 | 线上 |
摘要 | Breast cancer diagnosis in multi-gigapixel whole slide images (WSIs) is an important task that highly relevant to cancer grading and prognosis. In recent years, many computer-aided diagnosis methods were proposed and achieved promising performance. However, they mostly suffer from heavy computational burden that becomes a significant barrier to clinical practice. Efficient solutions are urgently demanded but still less studied. In this paper, we propose a novel framework named EffiDiag for a fast and lightweight breast cancer diagnosis. To this end, a loss-modified U-net is developed at first to enable a fast suspected cancer Region Of Interest (ROI) localization. Therefore the subsequent patch-based classification, which commonly executes at the finest magnification hundreds of thousands times per WSI for cancer identification, could be carried out on these ROIs only rather than the whole WSI for speedup. Meanwhile, a super-efficient convolutional neural network (CNN) is devised to optimize the classification speed and resource consumption per classification. Experiments on the Camelyon16 benchmark demonstrate, by integrating the two contributions into a well-established approach, 47x inference acceleration is obtained with limited accuracy drop, yet with much less resource consumption even compared to popular lightweight networks. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48538 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Zhineng Chen; Xuanya Li |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 3.湘潭大学计算机学院 4.百度 5.湘南学院医学影像与检验学院 |
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 | Shuyan Liu,Junda Ren,Zhineng Chen,et al. EffiDiag: an Efficient Framework for Breast Cancer Diagnosis in Multi-Gigapixel Whole Slide Images[C],2020:663-669. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[BIBM'20]EffiDiag- a(290KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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