Weakly Supervised Learning of Oral Cavity Images for Separating Precancer from Cancer

Early detection is crucial for reducing the morbidity and mortality associated with oral cancer. Visual examination of the oral cavity is the first step in screening for lesions that are cancer or precancer (i.e., the direct precursor to cancer). In contrast to existing work that uses image or lesion labels, we explore a weakly supervised method to address the situation where only patient-level labels are available. Specifically, we use multiple-instance learning (MIL) to classify patients with oral precancer versus cancer. Our approach involves three steps: image filtering, mouth detection, and patient-level classification. The method performs well on filtering and mouth detection and shows promising results in distinguishing precancer from cancer on an independent test set, demonstrating its potential as a proof-of-concept (PoC). We also discuss current limitations and future directions.