Xue, ZhiyunZhiyunXueYu, KellyKellyYuPearlman, Paul CPaul CPearlmanPal, AnabikAnabikPalTSENG-CHENG CHENHua, Chun-HungChun-HungHuaKang, Chung JanChung JanKangChien, Chih-YenChih-YenChienTsai, Ming-HsuiMing-HsuiTsaiCHENG-PING WANGChaturvedi, Anil KAnil KChaturvediAntani, SameerSameerAntani2023-11-102023-11-102022-0797817281278281557170Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/637084Intelligent 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.en[SDGs]SDG3journal article10.1109/EMBC48229.2022.9871610360865422-s2.0-85138128083https://api.elsevier.com/content/abstract/scopus_id/85138128083