159 research outputs found
Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline
Chest x-rays are the most common radiology studies for diagnosing lung and
heart disease. Hence, a system for automated pre-reporting of pathologic
findings on chest x-rays would greatly enhance radiologists' productivity. To
this end, we investigate a deep-learning framework with novel training schemes
for classification of different thoracic pathology labels from chest x-rays. We
use the currently largest publicly available annotated dataset ChestX-ray14 of
112,120 chest radiographs of 30,805 patients. Each image was annotated with
either a 'NoFinding' class, or one or more of 14 thoracic pathology labels.
Subjects can have multiple pathologies, resulting in a multi-class, multi-label
problem. We encoded labels as binary vectors using k-hot encoding. We study the
ResNet34 architecture, pre-trained on ImageNet, where two key modifications
were incorporated into the training framework: (1) Stochastic gradient descent
with momentum and with restarts using cosine annealing, (2) Variable image
sizes for fine-tuning to prevent overfitting. Additionally, we use a heuristic
algorithm to select a good learning rate. Learning with restarts was used to
avoid local minima. Area Under receiver operating characteristics Curve (AUC)
was used to quantitatively evaluate diagnostic quality. Our results are
comparable to, or outperform the best results of current state-of-the-art
methods with AUCs as follows: Atelectasis:0.81, Cardiomegaly:0.91,
Consolidation:0.81, Edema:0.92, Effusion:0.89, Emphysema: 0.92, Fibrosis:0.81,
Hernia:0.84, Infiltration:0.73, Mass:0.85, Nodule:0.76, Pleural
Thickening:0.81, Pneumonia:0.77, Pneumothorax:0.89 and NoFinding:0.79. Our
results suggest that, in addition to using sophisticated network architectures,
a good learning rate, scheduler and a robust optimizer can boost performance.Comment: 6 pages, 1 figure, 2 table
Classification of interstitial lung disease patterns with topological texture features
Topological texture features were compared in their ability to classify
morphological patterns known as 'honeycombing' that are considered indicative
for the presence of fibrotic interstitial lung diseases in high-resolution
computed tomography (HRCT) images. For 14 patients with known occurrence of
honey-combing, a stack of 70 axial, lung kernel reconstructed images were
acquired from HRCT chest exams. A set of 241 regions of interest of both
healthy and pathological (89) lung tissue were identified by an experienced
radiologist. Texture features were extracted using six properties calculated
from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and
three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN)
classifier and a Multilayer Radial Basis Functions Network (RBFN) were
optimized in a 10-fold cross-validation for each texture vector, and the
classification accuracy was calculated on independent test sets as a
quantitative measure of automated tissue characterization. A Wilcoxon
signed-rank test was used to compare two accuracy distributions and the
significance thresholds were adjusted for multiple comparisons by the
Bonferroni correction. The best classification results were obtained by the MF
features, which performed significantly better than all the standard GLCM and
MD features (p < 0.005) for both classifiers. The highest accuracy was found
for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively).
The best standard texture features were the GLCM features 'homogeneity' (91.8%,
87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced
topological texture features can provide superior classification performance in
computer-assisted diagnosis of interstitial lung diseases when compared to
standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201
Fast K-dimensional tree-structured vector quantization encoding method for image compression
This paper presents a fast K-dimensional tree-based search method to speed up the encoding process for vector quantization. The method is especially designed for very large codebooks and is based on a local search rather than on a global search including the whole feature space. The relations between the proposed method and several existing fast algorithms are discussed. Simulation results demonstrate that with little preprocessing and memory cost, the encoding time of the new algorithm has been reduced significantly while encoding quality remains the same with respect to other existing fast algorithms
Model-free functional MRI analysis based on unsupervised clustering
AbstractConventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the “neural gas” network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen’s self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both “neural gas” and the fuzzy clustering technique outperform Kohonen’s map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the “neural gas” outperforms the two other methods with respect to the quantization error, and (3) Kohonen’s map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data
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