Learning with Sure Data for Nodule-Level Lung Cancer Prediction

Recent evolution in image-based disease prediction based on deep learning has significantly extended the clinical capabilities of these systems. However, in certain cases (e.g. lung nodule prediction), ground truth labels manually annotated by radiologists (unsure data) are often based on subjective assessment, which lack pathological-proven benchmarks (sure data) at the nodule-level. To address this issue, we build a small yet definite CT dataset (171 patients) called SCH-LND focusing on solid lung nodules (90 benign/90 malignant cases). Under the supervision of SCH-LND dataset, many hidden drawbacks of unsure data (484 solid nodules selected from LIDC-IDRI dataset) served for malignancy prediction are objectively revealed. Explanations to this phenomenon are inferred in this paper from the view of model training and data annotation bias. Although learning from scratch over sure data with commonly used model can surpass the performance of unsure data in large scales, we additionally propose two frameworks to make the best use of these cross-domain resources, among which, transfer learning is verified as an effective approach for LIDC-IDRI knowledge adaptation. Results show that the proposed method can achieve good performance for nodule-level malignancy prediction with a small SCH-LND dataset.