| 한글제목(Korean Title) |
An Effective WSSENet-Based Similarity Retrieval Method of Large Lung CT Image Databases |
| 영문제목(English Title) |
An Effective WSSENet-Based Similarity Retrieval Method of Large Lung CT Image Databases |
| 저자(Author) |
Yi Zhuang
Shuai Chen
Nan Jiang
Hua Hu
|
| 원문수록처(Citation) |
VOL 16 NO. 07 PP. 2359 ~ 2376 (2022. 07) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed. |
| 키워드(Keyword) |
CT images
spatial transformer layer
Deep Learning
Content-based medical image retrieval
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| 파일첨부 |
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