| 1. |
- Kolbe, Viktor, et al.
(författare)
-
Indoor Photorealistic 3D Mapping using Stereo Images from SLR Cameras
- 2009
-
Ingår i: Proceedings of the '09 Swedish Symposium on Image Analysis (SSBA). - Linköping : Linköping University Electronic Press. - 9789163339240
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Creating a 3D model from photos require an estimate of the position and orientation (pose) of the camera for each photo that is acquired. This paper presents a method to estimate the camera pose using only image data. The images are acquired at a low frequency using a stereo rig, consisting of two rigidly attached SLR cameras. Features are extracted and an optimization problem is solved for each new stereo image. The results are used to merge multiple stereo images and building a larger model of the scene. The accumulated error after processing 10 images can with the present methods be less than 1.2 mm in translation and 0.1 degrees in rotation.
|
|
| 2. |
- Gustafsson, Stefan, et al.
(författare)
-
Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
- 2022
-
Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12
-
Tidskriftsartikel (refereegranskat)abstract
- Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients. We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained because of their presenting complaint. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs to the model were ECG tracing, age, and sex; and outputs were the probabilities of three mutually exclusive classes: non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status, as registered in the SWEDEHEART and other registries. We used an ensemble of five models. Among 492,226 ECGs in 214,250 patients, 5,416 were recorded with an NSTEMI, 1,818 a STEMI, and 485,207 without a myocardial infarction. In a random test set, our model could discriminate STEMIs/NSTEMIs from controls with a C-statistic of 0.991/0.832 and had a Brier score of 0.001/0.008. The model obtained a similar performance in a temporally separated test set of the study sample, and achieved a C-statistic of 0.985 and a Brier score of 0.002 in discriminating STEMIs from controls in an external test set. We developed and validated a deep learning model with excellent performance in discriminating between control, STEMI, and NSTEMI on the presenting ECG of a real-world sample of the important population of all-comers to the emergency department. Hence, deep learning models for ECG decision support could be valuable in the emergency department.
|
|
| 3. |
- Lima, Emilly M., et al.
(författare)
-
Deep neural network-estimated electrocardiographic age as a mortality predictor
- 2021
-
Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information. The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information.
|
|
| 4. |
- Lindsten, Fredrik, 1984-, et al.
(författare)
-
An Explicit Variance Reduction Expression for the Rao-Blackwellised Particle Filter
- 2010
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Particle filters (PFs) have shown to be very potent tools for state estimation in nonlinear and/or non-Gaussian state-space models. For certain models, containing a conditionally tractable substructure (typically conditionally linear Gaussian or with finite support), it is possible to exploit this structure in order to obtain more accurate estimates. This has become known as Rao-Blackwellised particle filtering (RBPF). However, since the RBPF is typically more computationally demanding than the standard PF per particle, it is not always beneficial to resort to Rao-Blackwellisation. For the same computational effort, a standard PF with an increased number of particles, which would also increase the accuracy, could be used instead. In this paper, we have analysed the asymptotic variance of the RBPF and provide an explicit expression for the obtained variance reduction. This expression could be used to make an efficient discrimination of when to apply Rao-Blackwellisation, and when not to.
|
|
| 5. |
- Murray, Lawrence, et al.
(författare)
-
Delayed sampling and automatic Rao-Blackwellization of probabilistic programs
- 2018
-
Ingår i: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April, 2018. - : PMLR.
-
Konferensbidrag (refereegranskat)abstract
- We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locallyoptimal proposals and Rao–Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linearnonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a randomweight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.
|
|
| 6. |
|
|
| 7. |
- Ancuti, Codruta O., et al.
(författare)
-
NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report
- 2023
-
Ingår i: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). - Vancover : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams that competed in the final testing phase, and the proposed solutions demonstrated the cutting-edge in image dehazing technology.
|
|
| 8. |
- Andersson, Carl
(författare)
-
Deep learning applied to system identification : A probabilistic approach
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification.Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes.Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.
|
|
| 9. |
- Andersson, Carl
(författare)
-
Deep probabilistic models for sequential and hierarchical data
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Consider the problem where we want a computer program capable of recognizing a pedestrian on the road. This could be employed in a car to automatically apply the brakes to avoid an accident. Writing such a program is immensely difficult but what if we could instead use examples and let the program learn what characterizes a pedestrian from the examples. Machine learning can be described as the process of teaching a model (computer program) to predict something (the presence of a pedestrian) with help of data (examples) instead of through explicit programming.This thesis focuses on a specific method in machine learning, called deep learning. This method can arguably be seen as sole responsible for the recent upswing of machine learning in academia as well as in society at large. However, deep learning requires, in human standards, a huge amount of data to perform well which can be a limiting factor. In this thesis we describe different approaches to reduce the amount of data that is needed by encoding some of our prior knowledge about the problem into the model. To this end we focus on sequential and hierarchical data, such as speech and written language.Representing sequential output is in general difficult due to the complexity of the output space. Here, we make use of a probabilistic approach focusing on sequential models in combination with a deep learning structure called the variational autoencoder. This is applied to a range of different problem settings, from system identification to speech modeling.The results come in three parts. The first contribution focus on applications of deep learning to typical system identification problems, the intersection between the two areas and how they can benefit from each other. The second contribution is on hierarchical data where we promote a multiscale variational autoencoder inspired by image modeling. The final contribution is on verification of probabilistic models, in particular how to evaluate the validity of a probabilistic output, also known as calibration.
|
|
| 10. |
- Andersson, Carl R., et al.
(författare)
-
Learning deep autoregressive models for hierarchical data
- 2021
-
Ingår i: IFAC PapersOnLine. - : Elsevier. - 2405-8963. ; , s. 529-534
-
Konferensbidrag (refereegranskat)abstract
- We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.
|
|
