The Experts below are selected from a list of 990 Experts worldwide ranked by ideXlab platform
Malte Probst - One of the best experts on this subject based on the ideXlab platform.
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Denoising Autoencoders for fast combinatorial black box optimization
Genetic and Evolutionary Computation Conference, 2015Co-Authors: Malte ProbstAbstract:We integrate a Denoising Autoencoder (DAE) into an Estimation of Distribution Algorithm (EDA) and evaluate the performance of DAE-EDA on several combinatorial optimization problems. We asses the number of fitness evaluations and the required CPU times. Compared to the state-of-the-art Bayesian Optimization Algorithm (BOA), DAE-EDA needs more fitness evaluations, but is considerably faster, sometimes by orders of magnitude. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.
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Denoising Autoencoders for fast combinatorial black box optimization
Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015Co-Authors: Malte ProbstAbstract:Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.
Enzo Ferrante - One of the best experts on this subject based on the ideXlab platform.
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post dae anatomically plausible segmentation via post processing with Denoising Autoencoders
IEEE Transactions on Medical Imaging, 2020Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Ben Glocker, Enzo FerranteAbstract:We introduce Post-DAE, a post-processing method based on Denoising Autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convolutional neural networks or random forest classifiers) incorporate additional post-processing steps to ensure that the resulting masks fulfill expected connectivity constraints. These methods operate under the hypothesis that contiguous pixels with similar aspect should belong to the same class. Even if valid in general, this assumption does not consider more complex priors like topological restrictions or convexity, which cannot be easily incorporated into these methods. Post-DAE leverages the latest developments in manifold learning via Denoising Autoencoders. First, we learn a compact and non-linear embedding that represents the space of anatomically plausible segmentations. Then, given a segmentation mask obtained with an arbitrary method, we reconstruct its anatomically plausible version by projecting it onto the learnt manifold. The proposed method is trained using unpaired segmentation mask, what makes it independent of intensity information and image modality. We performed experiments in binary and multi-label segmentation of chest X-ray and cardiac magnetic resonance images. We show how erroneous and noisy segmentation masks can be improved using Post-DAE. With almost no additional computation cost, our method brings erroneous segmentations back to a feasible space.
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anatomical priors for image segmentation via post processing with Denoising Autoencoders
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Enzo FerranteAbstract:Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions.
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anatomical priors for image segmentation via post processing with Denoising Autoencoders
arXiv: Image and Video Processing, 2019Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Enzo FerranteAbstract:Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on Denoising Autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.
Casey S Greene - One of the best experts on this subject based on the ideXlab platform.
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semi supervised learning of the electronic health record for phenotype stratification
Journal of Biomedical Informatics, 2016Co-Authors: Brett K Beaulieujones, Casey S GreeneAbstract:Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes, which can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using Denoising Autoencoders for phenotype stratification. By combining Denoising Autoencoders with random forests we find classification improvements across multiple simulation models and improved survival prediction in ALS clinical trial data. This is particularly evident in cases where only a small number of patients have high quality phenotypes, a common scenario in EHR-based research. Denoising Autoencoders perform dimensionality reduction enabling visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs.
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semi supervised learning of the electronic health record with Denoising Autoencoders for phenotype stratification
bioRxiv, 2016Co-Authors: Brett K Beaulieujones, Casey S GreeneAbstract:Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes that can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using Denoising Autoencoders for phenotype stratification. By combining Denoising Autoencoders with random forests we find classification improvements across simulation models, particularly in cases where only a small number of patients have high quality phenotype. This situation is commonly encountered in research with EHRs. Denoising Autoencoders perform dimensionality reduction allowing visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs.
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unsupervised feature construction and knowledge extraction from genome wide assays of breast cancer with Denoising Autoencoders
Pacific Symposium on Biocomputing, 2014Co-Authors: Chao Cheng, Casey S GreeneAbstract:Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. Here we present Denoising Autoencoders (DAs), which employ a data-defined learning objective independent of known biology, as a method to identify and extract complex patterns from genomic data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. There are features that represent tumor or normal samples, estrogen receptor (ER) status, and molecular subtypes. Features constructed by the autoencoder generalize to an independent dataset collected using a distinct experimental platform. By integrating data from ENCODE for feature interpretation, we discover a feature representing ER status through association with key transcription factors in breast cancer. We also identify a feature highly predictive of patient survival and it is enriched by FOXM1 signaling pathway. The features constructed by DAs are often bimodally distributed with one peak near zero and another near one, which facilitates discretization. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.
Agostina J Larrazabal - One of the best experts on this subject based on the ideXlab platform.
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post dae anatomically plausible segmentation via post processing with Denoising Autoencoders
IEEE Transactions on Medical Imaging, 2020Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Ben Glocker, Enzo FerranteAbstract:We introduce Post-DAE, a post-processing method based on Denoising Autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convolutional neural networks or random forest classifiers) incorporate additional post-processing steps to ensure that the resulting masks fulfill expected connectivity constraints. These methods operate under the hypothesis that contiguous pixels with similar aspect should belong to the same class. Even if valid in general, this assumption does not consider more complex priors like topological restrictions or convexity, which cannot be easily incorporated into these methods. Post-DAE leverages the latest developments in manifold learning via Denoising Autoencoders. First, we learn a compact and non-linear embedding that represents the space of anatomically plausible segmentations. Then, given a segmentation mask obtained with an arbitrary method, we reconstruct its anatomically plausible version by projecting it onto the learnt manifold. The proposed method is trained using unpaired segmentation mask, what makes it independent of intensity information and image modality. We performed experiments in binary and multi-label segmentation of chest X-ray and cardiac magnetic resonance images. We show how erroneous and noisy segmentation masks can be improved using Post-DAE. With almost no additional computation cost, our method brings erroneous segmentations back to a feasible space.
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anatomical priors for image segmentation via post processing with Denoising Autoencoders
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Enzo FerranteAbstract:Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions.
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anatomical priors for image segmentation via post processing with Denoising Autoencoders
arXiv: Image and Video Processing, 2019Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Enzo FerranteAbstract:Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on Denoising Autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.
Ben Glocker - One of the best experts on this subject based on the ideXlab platform.
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post dae anatomically plausible segmentation via post processing with Denoising Autoencoders
IEEE Transactions on Medical Imaging, 2020Co-Authors: Agostina J Larrazabal, Cesar E Martinez, Ben Glocker, Enzo FerranteAbstract:We introduce Post-DAE, a post-processing method based on Denoising Autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convolutional neural networks or random forest classifiers) incorporate additional post-processing steps to ensure that the resulting masks fulfill expected connectivity constraints. These methods operate under the hypothesis that contiguous pixels with similar aspect should belong to the same class. Even if valid in general, this assumption does not consider more complex priors like topological restrictions or convexity, which cannot be easily incorporated into these methods. Post-DAE leverages the latest developments in manifold learning via Denoising Autoencoders. First, we learn a compact and non-linear embedding that represents the space of anatomically plausible segmentations. Then, given a segmentation mask obtained with an arbitrary method, we reconstruct its anatomically plausible version by projecting it onto the learnt manifold. The proposed method is trained using unpaired segmentation mask, what makes it independent of intensity information and image modality. We performed experiments in binary and multi-label segmentation of chest X-ray and cardiac magnetic resonance images. We show how erroneous and noisy segmentation masks can be improved using Post-DAE. With almost no additional computation cost, our method brings erroneous segmentations back to a feasible space.