Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers

Abstract

This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The ensemble of classifiers (which can be considered as a computer panel of experts) uses 64 image features of the nodules across four categories (shape, intensity, texture, and size) to predict semantic characteristics. The active learning begins the training phase with nodules on which radiologists’ semantic ratings agree, and incrementally learns how to classify nodules on which the radiologists do not agree. Using our proposed approach, the classification accuracy of the ensemble of classifiers is higher than the accuracy of a single classifier. In the long run, our proposed approach can be used to increase consistency among radiological interpretations by providing physicians a “second read”

Analysis of shape features for lesion classification in breast ultrasound images

“…In [8], researchers are compared texture features and shape features of lung nodule including circularity, roughness, elongation, compactness, eccentricity, solidity, extent, and radial distance. With same object obtained from Lung Image Database Consortium (LIDC), Zinovev et al [9] are also used circularity, elongation, compactness, eccentricity and solidity as part of features extraction result.…”

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

“…The rating scale for spiculation in LIDC is from 1 to 5 with 5 being the most spiculated and 1 being least spiculated. Spiculation was not precisely defined by the LIDC, but the definition cited by several LIDC studies is Bthe degree to which the nodule exhibits spicules, spike-like structures, along its border [12][13][14]. Spiculated margin is commonly accepted as an indication of malignancy [15,16].…”

“…They include 9 intensity features and 40 texture features (11 Haralick features, 24 Gabor texture features, and 5 Markov features). Equations and descriptions of all texture and intensity features are provided in [14]. The intensity features used are the minimum, maximum, mean, and standard deviation of every pixel in the segmented region of the nodule and the same four values for every pixel in the background of the bounding box of the segmented region.…”

“…Eleven Haralick texture descriptors [43] are calculated as specified in [14] from each co-occurrence matrix: These descriptors are cluster tendency, contrast, correlation, energy, entropy, homogeneity, inverse variance, maximum probability, sum average, third-order moment, and variance. These descriptors are averaged by distance to obtain an average value associate with each of the four directions.…”

“…The intensity features used are the minimum, maximum, mean, and standard deviation of every pixel in the segmented region of the nodule and the same four values for every pixel in the background of the bounding box of the segmented region. In addition, an intensity difference feature was calculated as the absolute value of the difference of the mean of the intensity values of the pixels in the background and the mean of the intensity value of the pixels in the segmented region [14].…”