Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening.

Xue Z, Yu K, Pearlman PC, Pal A, Chen TC, Hua CH, Kang CJ, Chien CY, Tsai MH, Wang CP, Chaturvedi AK, Antani S

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3218-3221. doi: 10.1109/EMBC48229.2022.9871610.

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Abstract:

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.

Xue Z, Yu K, Pearlman PC, Pal A, Chen TC, Hua CH, Kang CJ, Chien CY, Tsai MH, Wang CP, Chaturvedi AK, Antani S. Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening. 
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3218-3221. doi: 10.1109/EMBC48229.2022.9871610.

PMID | PMCID | URL: https://doi.org/10.1109/embc48229.2022.9871610