Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening
Zhiyun Xue, Kelly J. Yu, Paul C. Pearlman, Anabik Pal, Tseng‐Cheng Chen, Chun‐Hung Hua, Chung Jan Kang, Chih-Yen Chien, Ming‐Hsui Tsai, Cheng‐Ping Wang, Anil K. Chaturvedi, Sameer Antani · 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · 2022
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.