19 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
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
The success of current deep saliency detection methods heavily depends on the
availability of large-scale supervision in the form of per-pixel labeling. Such
supervision, while labor-intensive and not always possible, tends to hinder the
generalization ability of the learned models. By contrast, traditional
handcrafted features based unsupervised saliency detection methods, even though
have been surpassed by the deep supervised methods, are generally
dataset-independent and could be applied in the wild. This raises a natural
question that "Is it possible to learn saliency maps without using labeled data
while improving the generalization ability?". To this end, we present a novel
perspective to unsupervised saliency detection through learning from multiple
noisy labeling generated by "weak" and "noisy" unsupervised handcrafted
saliency methods. Our end-to-end deep learning framework for unsupervised
saliency detection consists of a latent saliency prediction module and a noise
modeling module that work collaboratively and are optimized jointly. Explicit
noise modeling enables us to deal with noisy saliency maps in a probabilistic
way. Extensive experimental results on various benchmarking datasets show that
our model not only outperforms all the unsupervised saliency methods with a
large margin but also achieves comparable performance with the recent
state-of-the-art supervised deep saliency methods.Comment: Accepted by IEEE/CVF CVPR 2018 as Spotligh