8,637 research outputs found
Estimating Brain Age with Global and Local Dependencies
The brain age has been proven to be a phenotype of relevance to cognitive
performance and brain disease. Achieving accurate brain age prediction is an
essential prerequisite for optimizing the predicted brain-age difference as a
biomarker. As a comprehensive biological characteristic, the brain age is hard
to be exploited accurately with models using feature engineering and local
processing such as local convolution and recurrent operations that process one
local neighborhood at a time. Instead, Vision Transformers learn global
attentive interaction of patch tokens, introducing less inductive bias and
modeling long-range dependencies. In terms of this, we proposed a novel network
for learning brain age interpreting with global and local dependencies, where
the corresponding representations are captured by Successive Permuted
Transformer (SPT) and convolution blocks. The SPT brings computation efficiency
and locates the 3D spatial information indirectly via continuously encoding 2D
slices from different views. Finally, we collect a large cohort of 22645
subjects with ages ranging from 14 to 97 and our network performed the best
among a series of deep learning methods, yielding a mean absolute error (MAE)
of 2.855 in validation set, and 2.911 in an independent test set
Convolutional Neural Network based Age Estimation from Facial Image and Depth Prediction from Single Image
Convolutional neural network (CNN), one of the most commonly used
deep learning methods, has been applied to various computer
vision and pattern recognition tasks, and has achieved
state-of-the-art performance. Most recent research work on CNN
focuses on the innovations of the structure. This thesis explores
both the innovation of structure and final label encoding of CNN.
To evaluate the performance of our proposed network structure and
label encoding method, two computer vision tasks are conducted,
namely age estimation from facial image and depth estimation from
a single image.
For age estimation from facial image, we propose a novel
hierarchical aggregation based deep network to learn aging
features from facial images and apply our encoding method to
transfer the discrete aging labels into a possibility label,
which enables the CNN to conduct a classification task rather
than regression task. In contrast to traditional aging features,
where identical filter is applied to the en-
tire facial image, our deep aging feature can capture both local
and global cues in aging. Under our formulation, convolutional
neural network (CNN) is employed to extract region specific
features at lower layers. Then, low layer features are
hierarchically aggregated by using fully connected way to
consecutive higher layers. The resultant aging feature is of
dimensionality 110, which achieves both good discriminative
ability and efficiency. Experimental results of age prediction on
the MORPH-II and the FG-NET databases show that the proposed deep
aging feature outperforms state-of-the-art aging features by a
margin.
Depth estimation from a single image is an essential component
toward understanding the 3D geometry of a scene. Compared with
depth estimation from stereo images, depth map estimation from a
single image is an extremely challenging task. This thesis
addresses this task by regression with deep features, combined
with surface normal constrained depth refinement. The proposed
framework consists of two steps. First, we implement a
convolutional neural network (CNN) to learn the mapping from
multi-scale image patches to depth on the super-pixel level. In
this step, we apply the proposed label encoding method to
transfer the continuous depth labels to be possibility vectors,
which reformulates the regression task to a classification task.
Second, we refine predicted depth at the super-pixel level to the
pixel level by exploiting surface normal constraints on depth
map. Experimental results of depth estimation on the NYU2 dataset
show that the proposed method achieves a promising performance
and has a better performance compared
with methods without the proposed label encoding.
The above tasks show the proposed label encoding method has
promising performance, which is another direction of CNN
structure optimization
A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis
Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways
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