4 research outputs found
Factors determining generalization in deep learning models for scoring COVID-CT images
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement . Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Lung cancer is the leading cause of cancer death and early diagnosis is
associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive
imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to
distinguish from vascular and bone structures using CXR. Computer vision has
previously been proposed to assist human radiologists in this task, however,
leading studies use down-sampled images and computationally expensive methods
with unproven generalization. Instead, this study localizes lung nodules using
efficient encoder-decoder neural networks that process full resolution images
to avoid any signal loss resulting from down-sampling. Encoder-decoder networks
are trained and tested using the JSRT lung nodule dataset. The networks are
used to localize lung nodules from an independent external CXR dataset.
Sensitivity and false positive rates are measured using an automated framework
to eliminate any observer subjectivity. These experiments allow for the
determination of the optimal network depth, image resolution and pre-processing
pipeline for generalized lung nodule localization. We find that nodule
localization is influenced by subtlety, with more subtle nodules being detected
in earlier training epochs. Therefore, we propose a novel self-ensemble model
from three consecutive epochs centered on the validation optimum. This ensemble
achieved a sensitivity of 85% in 10-fold internal testing with false positives
of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6
following morphological false positive reduction. This result is comparable to
more computationally complex systems based on linear and spatial filtering, but
with a sub-second inference time that is faster than other methods. The
proposed algorithm achieved excellent generalization results against an
external dataset with sensitivity of 77% at a false positive rate of 7.6