Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network

By Fangzhou Liao, Ming Liang, Zhe Li, Xiaolin Hu, Sen Song. IEEE Transactions on Neural Networks and Learning Systems, 2017

468 citations recorded by the source corpus.

Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-OR gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/707ef87813de2c792820121c23868fab569ec559

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography (2019, methodology): “While our radiologist-comparison studies were larger than in prior published work 32 , they were still limited to retrospective data from the NLST dataset.”
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT (2020, methodology): “To obtain the ground-truth lung masks, we segmented the lung regions using an unsupervised learning method [27], removed the failure cases manually, and the rest segmentation results were taken as ground-truth masks.”
Deep Learning Applications in Medical Image Analysis (2018, background): “Approximately 2000 CT scans were released for the competition and the winner Fangzhou [65] achieved a logarithmic loss score of 0.”
Thoracic Disease Identification and Localization with Limited Supervision (2017, background): “3Later on, we notice an similar definition [20] for this multi-instance problem.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “The most sophisticated and safest way of analyzing the medical images is through computer vision techniques [13], [14].”
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification (2018, methodology): “use the Faster R-CNN to generate candidate nodules followed by 3D ConvNets to remove false positive nodules [5, 17].”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “In the radiology domain, the intrinsic 3D spatial context information among volumetric images (e.g. CT scans) has been utilized in many studies (Roth et al., 2016; Dou et al., 2017; Yan et al., 2018a; Liao et al., 2019).”
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “…networks (CNNs), near-radiologist level performance can be achieved in automated medical image analysis tasks including classification of Alzheimer’s disease (Sarraf et al., 2017), skin lesions (Esteva et al., 2017; Kawahara and Hamarneh, 2016) and echo-cardiogram views (Madani et al., 2018), lung…”
Transformers in Medical Imaging: A Survey (2022, methodology): “Following the success of CNNs in medical image detection [278], [279], recently few attempts have been made to improve performance further using Transformer models.”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “Another interesting work in Liao, Liang, Li, Hu, and Song (2019) proposed a novel lung cancer detection model.”