2,211 research outputs found
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules
Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand
An automated system for lung nodule detection in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, a dot-enhancement
filter for nodule candidate selection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging
Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514,
65143
Highly accurate model for prediction of lung nodule malignancy with CT scans
Computed tomography (CT) examinations are commonly used to predict lung
nodule malignancy in patients, which are shown to improve noninvasive early
diagnosis of lung cancer. It remains challenging for computational approaches
to achieve performance comparable to experienced radiologists. Here we present
NoduleX, a systematic approach to predict lung nodule malignancy from CT data,
based on deep learning convolutional neural networks (CNN). For training and
validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.
All nodules were identified and classified by four experienced thoracic
radiologists who participated in the LIDC project. NoduleX achieves high
accuracy for nodule malignancy classification, with an AUC of ~0.99. This is
commensurate with the analysis of the dataset by experienced radiologists. Our
approach, NoduleX, provides an effective framework for highly accurate nodule
malignancy prediction with the model trained on a large patient population. Our
results are replicable with software available at
http://bioinformatics.astate.edu/NoduleX
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.Comment: 29 pages, 4 figure
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