Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
This work addresses the challenge of supporting radiologists in longitudinal lung cancer management, though it appears incremental as it builds on existing deep learning approaches with specific novel components.
The authors tackled the problem of automating lung cancer management by developing a deep learning pipeline that detects pulmonary nodules, quantifies their growth, and predicts malignancy in follow-up CT scans, achieving performance comparable to state-of-the-art methods.
We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN network the estimated nodule malignancy probabilities derived from a pretrained nodule malignancy network. The pipeline was evaluated in a longitudinal cohort and reported comparable performances to the state of art.