https://scholars.lib.ntu.edu.tw/handle/123456789/637084
標題: | Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening | 作者: | Xue, Zhiyun Yu, Kelly Pearlman, Paul C Pal, Anabik TSENG-CHENG CHEN Hua, Chun-Hung Kang, Chung Jan Chien, Chih-Yen Tsai, Ming-Hsui CHENG-PING WANG Chaturvedi, Anil K Antani, Sameer |
公開日期: | 七月-2022 | 卷: | 2022 | 來源出版物: | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference | 摘要: | Intelligent computer-aided algorithms analyzing photographs of various mouth regions can help in reducing the high subjectivity in human assessment of oral lesions. Very often, in the images, a ruler is placed near a suspected lesion to indicate its location and as a physical size reference. In this paper, we compared two deep-learning networks: ResNeSt and ViT, to automatically identify ruler images. Even though the ImageN et 1K dataset contains a "ruler" class label, the pre-trained models showed low sensitivity. After fine-tuning with our data, the two networks achieved high performance on our test set as well as a hold-out test set from a different provider. Heatmaps generated using three saliency methods: GradCam and XRAI for ResNeSt model, and Attention Rollout for ViT model, demonstrate the effectiveness of our technique. Clinical Relevance- This is a pre-processing step in automated visual evaluation for oral cancer screening. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637084 | ISBN: | 9781728127828 | ISSN: | 1557170X | DOI: | 10.1109/EMBC48229.2022.9871610 |
顯示於: | 醫學系 |
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