Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans

• URL: http://arxiv.org/abs/2103.14537v1
• Date: Fri, 26 Mar 2021 15:41:37 GMT
• Title: Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
• Authors: Xavier Rafael-Palou (1 and 2), Anton Aubanell (3), Mario Ceresa (2), Vicent Ribas (1), Gemma Piella (2) and Miguel A. Gonz\'alez Ballester (2 and 4) ((1) Eurecat Centre Tecnol\`ogic de Catalunya, eHealth Unit, Barcelona, Spain (2) BCN MedTech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (3) Vall d'Hebron University Hospital, Barcelona, Spain (4) ICREA, Barcelona, Spain)
• Abstract summary: The pipeline is composed of four stages that completely automatized from the detection of nodules to the classification of cancer. The pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. A second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN network the estimated malignancy probabilities derived from a pretrained nodule malignancy network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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