52 research outputs found
Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data
Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm’s capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today’s era of digitalization
Quantifying Data Augmentation for LiDAR based 3D Object Detection
In this work, we shed light on different data augmentation techniques
commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection.
We, therefore, utilize a state of the art voxel-based 3D Object Detection
pipeline called PointPillars and carry out our experiments on the well
established KITTI dataset. We investigate a variety of global and local
augmentation techniques, where global augmentation techniques are applied to
the entire point cloud of a scene and local augmentation techniques are only
applied to points belonging to individual objects in the scene. Our findings
show that both types of data augmentation can lead to performance increases,
but it also turns out, that some augmentation techniques, such as individual
object translation, for example, can be counterproductive and can hurt overall
performance. We show that when we apply our findings to the data augmentation
policy of PointPillars we can easily increase its performance by up to 2%. In
order to provide reproducibility, our code will be publicly available at
www.trace.ethz.ch/3D_Object_Detection
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
Monocular depth estimation is vital for scene understanding and downstream
tasks. We focus on the supervised setup, in which ground-truth depth is
available only at training time. Based on knowledge about the high regularity
of real 3D scenes, we propose a method that learns to selectively leverage
information from coplanar pixels to improve the predicted depth. In particular,
we introduce a piecewise planarity prior which states that for each pixel,
there is a seed pixel which shares the same planar 3D surface with the former.
Motivated by this prior, we design a network with two heads. The first head
outputs pixel-level plane coefficients, while the second one outputs a dense
offset vector field that identifies the positions of seed pixels. The plane
coefficients of seed pixels are then used to predict depth at each position.
The resulting prediction is adaptively fused with the initial prediction from
the first head via a learned confidence to account for potential deviations
from precise local planarity. The entire architecture is trained end-to-end
thanks to the differentiability of the proposed modules and it learns to
predict regular depth maps, with sharp edges at occlusion boundaries. An
extensive evaluation of our method shows that we set the new state of the art
in supervised monocular depth estimation, surpassing prior methods on NYU
Depth-v2 and on the Garg split of KITTI. Our method delivers depth maps that
yield plausible 3D reconstructions of the input scenes. Code is available at:
https://github.com/SysCV/P3DepthComment: Accepted at CVPR 202
Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision
processes is the design of efficient optimization methods with desirable
convergence and stability properties. To tackle this, we propose competitive
policy optimization (CoPO), a novel policy gradient approach that exploits the
game-theoretic nature of competitive games to derive policy updates. Motivated
by the competitive gradient optimization method, we derive a bilinear
approximation of the game objective. In contrast, off-the-shelf policy gradient
methods utilize only linear approximations, and hence do not capture
interactions among the players. We instantiate CoPO in two ways:(i) competitive
policy gradient, and (ii) trust-region competitive policy optimization. We
theoretically study these methods, and empirically investigate their behavior
on a set of comprehensive, yet challenging, competitive games. We observe that
they provide stable optimization, convergence to sophisticated strategies, and
higher scores when played against baseline policy gradient methods.Comment: 11 pages main paper, 6 pages references, and 31 pages appendix. 14
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