基于集成学习的 PET/CT 混合成像肺癌检测.

The issue of artificial intelligent detection for lung cancer in PET/CT hybrid imaging was investigated in this paper by using an integrated learning model based on multi-scale and multi-modality Mask R-CNN. Firstly, the candidate of lung cancer was extracted through three deep learning models. These three models were generated by appropriately tuning the Mask R-CNN employing certain training data that consisted of images from three different scales and different modalities. Then these three models were integrated using integrated learning of weight voting strategy to diminish the false positive outcomes.69 patients with lung cancer and 11 normal cases were included in the experiment.1242 slices with lung cancer were utilized for three training data sets.270 axial slices, including 58 PET slices and 58 CT slices with lung tumor and 77 PET normal slices and 77 CT normal slices, were used for testing. The F-score, precision and recall of resemble learning were 0.94,0.93,and 0.94,respectively.Compared with single model of Mask R-CNN and model of major voting, model of integrated learning based on multi-modality and multi-scale Mask R-CNN performed best. The experimental results showed this method is effective for the detection of lung cancer with PET/CT image, and can provide meaningful auxiliary diagnostic information for doctors. [ABSTRACT FROM AUTHOR]