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Hyperspectral microscopic imaging of skin squamous cell carcinoma
Sheng, Zhenfei1; Zhang, Xiaofa1; Qiu, Zelong1; Zhang, Chunguang1,2; Wang, Hao1,2; Huang, Xi1; Tan, Zhiwei1; Qiu, Weijie1; Wang, Pengchong1,2; Liu, Wenyao3; Duan MQ(段茂强)1,4; Huang, Xiaoli1,5; Liu, Yiping1; Xing, Yuwei1; Lin, Binbin1
Department工业控制网络与系统研究室
Conference Name14th National Conference on Laser Technology and Optoelectronics, LTO 2019
Conference DateMarch 17-20, 2019
Conference PlaceShanghai, China
Source Publication14th National Conference on Laser Technology and Optoelectronics, LTO 2019
PublisherSPIE
Publication PlaceBellingham, USA
2019
Pages1-6
Indexed ByEI ; CPCI(ISTP)
EI Accession number20193107262599
WOS IDWOS:000489752300076
Contribution Rank4
ISSN0277-786X
ISBN978-1-5106-3046-8
KeywordAOTF hyperspectral imaging object detection data augmentation deep learning
AbstractAcousto-optic tunable filter (AOTF) is a new type of light splitter with fast tuning, stable structure and portability. In this paper, a hyperspectral microscopic imaging system is constructed by combining non-collinear AOTF with optical inverted microscopy. The feasibility of data augmentation based on hyperspectral images for object detection of skin squamous cell carcinoma is studied. The hyperspectral images collected from unstained sections of skin squamous cell carcinoma are processed into dataset. At the same time, the mature open source object detection model is selected and trained for 20,000 times. Using the trained model to detect the lesion area of other unstained sections, it is found that the model trained by hyperspectral image dataset has a good ability to distinguish the non-lesion area, and there is no false detection. And the model has a relatively accurate detection ability for large lesion area, but the results of the model for small lesion area are not ideal. After analysis, it is considered that the number of samples can be increased firstly, especially in small lesions, and the same to the hyperspectral images. In addition, the model for lesion detection can be further optimized. By increasing the complexity of the model, the model can learn more details and information in the image during the training process. The preliminary results of the experiment prove that hyperspectral imaging is feasible for data augmentation of lesion object detection dataset. This paper provides a new method for the object detection data augmentation of skin squamous cell carcinoma.
Language英语
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25391
Collection工业控制网络与系统研究室
Corresponding AuthorZhang, Chunguang; Wang, Hao; Wang, Pengchong
Affiliation1.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
2.CAS Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences, Xi'an 710119, China
3.Fujian Normal University Hospital, Fuzhou 350007, China
4.Key Lab of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
5.Institute of Scientific and Technical Information of Liaoning Province, Shenyang 110181, China
Recommended Citation
GB/T 7714
Sheng, Zhenfei,Zhang, Xiaofa,Qiu, Zelong,et al. Hyperspectral microscopic imaging of skin squamous cell carcinoma[C]. Bellingham, USA:SPIE,2019:1-6.
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