Beyond Dysplasia: Uncovering Structure in Oral Potentially Malignant Diseases with Unsupervised Contrastive Learning.

Automated cancer diagnosis research often focuses on a binary task - recognize dysplasia and cancer from other lesions. However, other clinical conditions have estimated malignant transformation rates. Grouping these oral potentially malignant diseases with benign conditions may not be ideal.

Understanding Bias in Multispectral Autofluorescence Lifetime Imaging: Are Models Sensitive to Oral Location?

While bias in artificial intelligence is gaining attention across applications, model fairness is especially concerning in medical applications because a person's health may depend on the model outcome. Sources of bias in medical applications include age, gender, race, and social history. However, in oral cancer diagnosis, the oral location may be a source of bias.