66,679 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
Deep Learning for Survival Analysis: A Review
The influx of deep learning (DL) techniques into the field of survival
analysis in recent years, coupled with the increasing availability of
high-dimensional omics data and unstructured data like images or text, has led
to substantial methodological progress; for instance, learning from such
high-dimensional or unstructured data. Numerous modern DL-based survival
methods have been developed since the mid-2010s; however, they often address
only a small subset of scenarios in the time-to-event data setting - e.g.,
single-risk right-censored survival tasks - and neglect to incorporate more
complex (and common) settings. Partially, this is due to a lack of exchange
between experts in the respective fields.
In this work, we provide a comprehensive systematic review of DL-based
methods for time-to-event analysis, characterizing them according to both
survival- and DL-related attributes. In doing so, we hope to provide a helpful
overview to practitioners who are interested in DL techniques applicable to
their specific use case as well as to enable researchers from both fields to
identify directions for future investigation. We provide a detailed
characterization of the methods included in this review as an open-source,
interactive table: https://survival-org.github.io/DL4Survival. As this research
area is advancing rapidly, we encourage the research community to contribute to
keeping the information up to date.Comment: 24 pages, 6 figures, 2 tables, 1 interactive tabl
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