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Computer aided endoscope diagnosis via weakly labeled data mining
Wang S(王帅); Cong Y(丛杨); Fan HJ(范慧杰); Yang YS(杨云生); Tang YD(唐延东); Zhao HC(赵怀慈)
Department机器人学研究室
Conference Name2015 IEEE International Conference on Image Processing (ICIP)
Conference DateSeptember 27-30, 2015
Conference PlaceQuebec City, QC, Canada
Source Publication2015 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages3072-3076
Indexed ByEI ; CPCI(ISTP)
EI Accession number20160601896363
WOS IDWOS:000371977803040
Contribution Rank1
ISBN978-1-4799-8339-1
KeywordComputer Aided Diagnosis (Cad) Multiple Instance Learning (Mil) Weakly Labeled Endoscope Images
AbstractIn comparison to most computer aided endoscope diagnosis methods using pixel-wise groundtruth by physicians manually, it is easy to get lots of endoscope images with corresponding diagnostic reports. In this paper, we intend to mine pixel-wise label information from these reports with weak frame-level labels automatically. To achieve this, we formulate our computer aided diagnosis problem as a Multiple Instance Learning (MIL) issue, where we represent each image as superpixels. Each image and each superpixel is cast as bag and instance, respectively. We then evaluate and select the most positive instances from positive bags automatically which helps us transform the frame-level classification problem into a standard supervised learning problem. In the experiment, we build a new gastroscopic image dataset with more than 3000 weakly labeled images, and ours outperforms the state-of-the-art methods, which verifies the effectiveness of our model.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17466
Collection机器人学研究室
Corresponding AuthorWang S(王帅)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.University of Chinese Academy of Sciences, China
3.Chinese PLA General Hospital, China
Recommended Citation
GB/T 7714
Wang S,Cong Y,Fan HJ,et al. Computer aided endoscope diagnosis via weakly labeled data mining[C]. Piscataway, NJ, USA:IEEE,2015:3072-3076.
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