Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.

ABSTRACT: Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias. The model integrates three components: (i) a Classification Stream, utilizing a CNN to categorize images into 16 lesion types (baseline model), (ii) a Guidance Stream, which aligns class activation maps with clinically relevant areas using ground truth segmentation masks (GAIN model), and (iii) an Anatomical Site Prediction Stream, improving interpretability by predicting lesion location (GAIN+ASP model). The development dataset comprised 2765 intra-oral dig

SUBMITTER: Patel A 

PROVIDER: S-EPMC11685657 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature