Adaptive Cross-View Feature Mining for Jaw Lesions Detection and Recognition in CBCT Image

Cone beam computed tomography (CBCT) plays a vital role in the jaw lesions clinical diagnosis. However, different types of jaw lesions exhibit similar appearances in CBCT slices, while existing computer-aided diagnostic models, neither 2D slices with lack of distinctive features or 3D volume with highly redundant information, resulting in limited performance. For better detection of jaw lesions, we proposed a novel cross-view feature mining detection network based on reinforcement learning to adaptively extract the most characteristic slices from multi-views. Specifically, for every transverse plane slice in the CBCT image, policy network is designed to extract these corresponding sagittal and coronal slices with the most critical features for lesion detection. And then these slices are encoded and fused into the recognition branch which enhanced the overall performance. In our experiments, the proposed network reached detection recall of 79.7%, precision of 89.2%, and high average precision (AP) of 0.84 with an intersection-over-union (IoU) of 0.5. Quantitative results show that the proposed network is more effective than existing advanced approaches in the clinical detection and recognition of jaw lesions.