645 research outputs found
Learning to detect chest radiographs containing lung nodules using visual attention networks
Machine learning approaches hold great potential for the automated detection
of lung nodules in chest radiographs, but training the algorithms requires vary
large amounts of manually annotated images, which are difficult to obtain. Weak
labels indicating whether a radiograph is likely to contain pulmonary nodules
are typically easier to obtain at scale by parsing historical free-text
radiological reports associated to the radiographs. Using a repositotory of
over 700,000 chest radiographs, in this study we demonstrate that promising
nodule detection performance can be achieved using weak labels through
convolutional neural networks for radiograph classification. We propose two
network architectures for the classification of images likely to contain
pulmonary nodules using both weak labels and manually-delineated bounding
boxes, when these are available. Annotated nodules are used at training time to
deliver a visual attention mechanism informing the model about its localisation
performance. The first architecture extracts saliency maps from high-level
convolutional layers and compares the estimated position of a nodule against
the ground truth, when this is available. A corresponding localisation error is
then back-propagated along with the softmax classification error. The second
approach consists of a recurrent attention model that learns to observe a short
sequence of smaller image portions through reinforcement learning. When a
nodule annotation is available at training time, the reward function is
modified accordingly so that exploring portions of the radiographs away from a
nodule incurs a larger penalty. Our empirical results demonstrate the potential
advantages of these architectures in comparison to competing methodologies
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
The lack of fine-grained annotations hinders the deployment of automated
diagnosis systems, which require human-interpretable justification for their
decision process. In this paper, we address the problem of weakly supervised
identification and localization of abnormalities in chest radiographs. To that
end, we introduce a novel loss function for training convolutional neural
networks increasing the \emph{localization confidence} and assisting the
overall \emph{disease identification}. The loss leverages both image- and
patch-level predictions to generate auxiliary supervision. Rather than forming
strictly binary from the predictions as done in previous loss formulations, we
create targets in a more customized manner, which allows the loss to account
for possible misclassification. We show that the supervision provided within
the proposed learning scheme leads to better performance and more precise
predictions on prevalent datasets for multiple-instance learning as well as on
the NIH~ChestX-Ray14 benchmark for disease recognition than previously used
losses
Longitudinal detection of radiological abnormalities with time-modulated LSTM
Convolutional neural networks (CNNs) have been successfully employed in
recent years for the detection of radiological abnormalities in medical images
such as plain x-rays. To date, most studies use CNNs on individual examinations
in isolation and discard previously available clinical information. In this
study we set out to explore whether Long-Short-Term-Memory networks (LSTMs) can
be used to improve classification performance when modelling the entire
sequence of radiographs that may be available for a given patient, including
their reports. A limitation of traditional LSTMs, though, is that they
implicitly assume equally-spaced observations, whereas the radiological exams
are event-based, and therefore irregularly sampled. Using both a simulated
dataset and a large-scale chest x-ray dataset, we demonstrate that a simple
modification of the LSTM architecture, which explicitly takes into account the
time lag between consecutive observations, can boost classification
performance. Our empirical results demonstrate improved detection of commonly
reported abnormalities on chest x-rays such as cardiomegaly, consolidation,
pleural effusion and hiatus hernia.Comment: Submitted to 4th MICCAI Workshop on Deep Learning in Medical Imaging
Analysi
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical
machine learning models. One strategy is to generate training labels
programmatically, for example by applying natural language processing pipelines
to text reports associated with imaging studies. We propose cross-modal data
programming, which generalizes this intuitive strategy in a
theoretically-grounded way that enables simpler, clinician-driven input,
reduces required labeling time, and improves with additional unlabeled data. In
this approach, clinicians generate training labels for models defined over a
target modality (e.g. images or time series) by writing rules over an auxiliary
modality (e.g. text reports). The resulting technical challenge consists of
estimating the accuracies and correlations of these rules; we extend a recent
unsupervised generative modeling technique to handle this cross-modal setting
in a provably consistent way. Across four applications in radiography, computed
tomography, and electroencephalography, and using only several hours of
clinician time, our approach matches or exceeds the efficacy of
physician-months of hand-labeling with statistical significance, demonstrating
a fundamentally faster and more flexible way of building machine learning
models in medicine
Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs
Due to the high complexity of medical images and the scarcity of trained personnel, most large-scale radiological datasets are lacking fine-grained annotations and are often only described on image-level. These shortcomings hinder the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs in a multiple-instance learning setting. To that end, we introduce a novel loss function for training convolutional neural networks increasing the localization confidence and assisting the overall disease identification. The loss leverages both image-and patch-level predictions to generate auxiliary supervision and enables specific training at patch-level. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner. This way, the loss accounts for possible misclassification of less certain instances. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH ChestX-Ray14 benchmark for disease recognition than previously used losses
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