2,364 research outputs found
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Leveraging Disease Progression Learning for Medical Image Recognition
Unlike natural images, medical images often have intrinsic characteristics
that can be leveraged for neural network learning. For example, images that
belong to different stages of a disease may continuously follow a certain
progression pattern. In this paper, we propose a novel method that leverages
disease progression learning for medical image recognition. In our method,
sequences of images ordered by disease stages are learned by a neural network
that consists of a shared vision model for feature extraction and a long
short-term memory network for the learning of stage sequences. Auxiliary vision
outputs are also included to capture stage features that tend to be discrete
along the disease progression. Our proposed method is evaluated on a public
diabetic retinopathy dataset, and achieves about 3.3% improvement in disease
staging accuracy, compared to the baseline method that does not use disease
progression learning
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Predicting response to neoadjuvant therapy is a vexing challenge in breast
cancer. In this study, we evaluate the ability of deep learning to predict
response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment
dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a
retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast
cancer patients from 5 institutions, we developed and validated a deep learning
approach for predicting pathological complete response (pCR) to HER2-targeted
NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant
chemotherapy at a single institution were used to train (n=85) and tune (n=15)
a convolutional neural network (CNN) to predict pCR. A multi-input CNN
leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was
identified to achieve optimal response prediction within the validation set
(AUC=0.93). This model was then tested on two independent testing cohorts with
pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient
testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and
a 29 patient multicenter trial including data from 3 additional institutions
(AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction
model was found to exceed a multivariable model incorporating predictive
clinical variables (AUC < .65 in testing cohorts) and a model of
semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing
cohorts). The results presented in this work across multiple sites suggest that
with further validation deep learning could provide an effective and reliable
tool to guide targeted therapy in breast cancer, thus reducing overtreatment
among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3
figures in main tex
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
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
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