Towards automatic pulmonary nodule management in lung cancer screening with deep learning
By Francesco Ciompi, Kaman Chung, S. Riel, Arnaud Arindra Adiyoso Setio, P. K. Gerke, C. Jacobs, E. Scholten, C. Schaefer-Prokop, M. Wille, A. Marchiano and 3 more. Scientific Reports, 2016
392 citations recorded by the source corpus.
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/fd0901b1f2121506391a7859de8fb695d159a393
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
- B. van Ginneken
- Francesco Ciompi
- J. A. van der Laak
- Geert J. S. Litjens
- Ara Darzi
- Dominic King
- Daniel Shu Wei Ting
- Alan Karthikesalingam
Citations in the corpus
- Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021, background): “These included large numbers of studies in the fields of neurology/neurosurgery (78), gastroenterology/hepatology (24) and urology (25).”
- Deep Learning for Medical Image Processing: Overview, Challenges and Future (2017, methodology): “Deep learning provides new cost effective to train DNN were slow in learning the weights.”
- Current Applications and Future Impact of Machine Learning in Radiology. (2018, positive): “For example, a deep learning system to classify pulmonary nodules performs within the interobserver variability of experienced human observers (41).”
- A survey on deep learning in medical image analysis (2017, methodology): “…(LIDC-IDRI data set) Chen et al. (2017) Combines features from CNN, SDAE and classical features to characterize nodules from LIDC-IDRI data set Ciompi et al. (2016) Multi-stream CNN to classify nodules into subtypes: solid, part-solid, non-solid, calcified, spiculated, perifissural Dou et al.…”
- Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning (2017, methodology): “Recently, Ciompi et al. [55] presented a method for inferring the nodule type with use of a convolutional network.”