2,763 research outputs found
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Clinical text classification is an important problem in medical natural
language processing. Existing studies have conventionally focused on rules or
knowledge sources-based feature engineering, but only a few have exploited
effective feature learning capability of deep learning methods. In this study,
we propose a novel approach which combines rule-based features and
knowledge-guided deep learning techniques for effective disease classification.
Critical Steps of our method include identifying trigger phrases, predicting
classes with very few examples using trigger phrases and training a
convolutional neural network with word embeddings and Unified Medical Language
System (UMLS) entity embeddings. We evaluated our method on the 2008
Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge.
The results show that our method outperforms the state of the art methods.Comment: arXiv admin note: text overlap with arXiv:1806.04820 by other author
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
The inability to interpret the model prediction in semantically and visually
meaningful ways is a well-known shortcoming of most existing computer-aided
diagnosis methods. In this paper, we propose MDNet to establish a direct
multimodal mapping between medical images and diagnostic reports that can read
images, generate diagnostic reports, retrieve images by symptom descriptions,
and visualize attention, to provide justifications of the network diagnosis
process. MDNet includes an image model and a language model. The image model is
proposed to enhance multi-scale feature ensembles and utilization efficiency.
The language model, integrated with our improved attention mechanism, aims to
read and explore discriminative image feature descriptions from reports to
learn a direct mapping from sentence words to image pixels. The overall network
is trained end-to-end by using our developed optimization strategy. Based on a
pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we
conduct sufficient experiments to demonstrate that MDNet outperforms
comparative baselines. The proposed image model obtains state-of-the-art
performance on two CIFAR datasets as well.Comment: CVPR2017 Ora
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