Elena Ericheva

M.D

botkin.ai

Lead Data Scientist

NLST (Learn more about this study)

NLST-508

May 8, 2019

Attention-based deep learning approach for simultaneous cancer risk assessment and nodule detection in CT lung screening

The new CAD-approach based deep learning model is proposed for lungs computer tomography screening task. The purpose of screening is to detect cancer at an earlier stage, when treatment options are better. Screening will mean a significant increase in reading efforts for radiologists. Automated detection systems (CADs) have been developed to help radiologists read the exam and thus potentially make lung cancer screening more economical. The cost-effectiveness of the lung cancer screening program is affected by the treatment protocol for pulmonary nodules [0]. Differentiation of high-risk nodules from low-risk nodules based on the characteristics of the nodules remains a challenge [1, 2]. Proposed screening model is a part of full computer-assisted medical image analysis pipeline used as CAD system for early lung cancer diagnosis. To perform over open sourced approaches as well as known challenges winners results (as benchmark) the model utilizes pretrained backbone from false positive reduction network architecture from the pipeline to initialize screening model, negative mining method as fine-tune step in FPR training process, attention gates as fusion layers. Weighting 3D attention in Detector scales the low level query signal in segmentation task by an element-wise product and retains only relevant activations.Ranging self-attention in Screening parts of the pipeline learns to compute the weights for each of these presentation before summing them together to deal well with inconstant number of inputs. Also model accepts multiple inputs which work as different receptive fields from input image. Farther we trained all parts of pipeline independently on different datasets: Detector and FPR networks were trained on LUNA16 while Screening should be trained on NLST. Although our results are promising, we recognize that the test suite used in our experiments is relatively small, and our results require further theoretical and clinical testing.


[0] Tammemagi MC, Cressman S, Lam S. Resource utilization and costs during the initial years of lung cancer screening with computed tomography in canada. J Thorac Oncol 9(10), pages 1449–1458,2, 2014.
[1] Nath PH, Kazerooni E, Amorosa J Pinsky PF, Gierada DS. National lung screening trial: variability in nodule detection rates in chest ct studies.Radiology 268(3), pages 865–873, 3, 2013.
[2] Fineberg NS et al Singh S, Pinsky P. Evaluation of reader variability in the interpretation of follow-up ct scans at lung cancer screening.Radiology 259(1), pages 263–270, 2011.