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Article ; Online: Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis.

Hu, Huafeng / Ye, Ruijie / Thiyagalingam, Jeyan / Coenen, Frans / Su, Jionglong

Applied intelligence (Dordrecht, Netherlands)

2023 , Page(s) 1–16

Abstract: In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a "bag" as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple ... ...

Abstract	In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a "bag" as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.
Language	English
Publishing date	2023-04-04
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479519-X
ISSN	1573-7497 ; 0924-669X
ISSN (online)	1573-7497
ISSN	0924-669X
DOI	10.1007/s10489-023-04458-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: On the Compatibility between Neural Networks and Partial Differential Equations for Physics-informed Learning

Leng, Kuangdai / Thiyagalingam, Jeyan

2022

Abstract: We shed light on a pitfall and an opportunity in physics-informed neural networks (PINNs). We prove that a multilayer perceptron (MLP) only with ReLU (Rectified Linear Unit) or ReLU-like Lipschitz activation functions will always lead to a vanished ... ...

Abstract	We shed light on a pitfall and an opportunity in physics-informed neural networks (PINNs). We prove that a multilayer perceptron (MLP) only with ReLU (Rectified Linear Unit) or ReLU-like Lipschitz activation functions will always lead to a vanished Hessian. Such a network-imposed constraint contradicts any second- or higher-order partial differential equations (PDEs). Therefore, a ReLU-based MLP cannot form a permissible function space for the approximation of their solutions. Inspired by this pitfall, we prove that a linear PDE up to the $n$-th order can be strictly satisfied by an MLP with $C^n$ activation functions when the weights of its output layer lie on a certain hyperplane, as called the out-layer-hyperplane. An MLP equipped with the out-layer-hyperplane becomes "physics-enforced", no longer requiring a loss function for the PDE itself (but only those for the initial and boundary conditions). Such a hyperplane exists not only for MLPs but for any network architecture tailed by a fully-connected hidden layer. To our knowledge, this should be the first PINN architecture that enforces point-wise correctness of PDEs. We show a closed-form expression of the out-layer-hyperplane for second-order linear PDEs, which can be generalised to higher-order nonlinear PDEs. Comment: 12 pages, 3 figures
Keywords	Physics - Computational Physics ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-11-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Disentangling Autoencoders (DAE)

Cha, Jaehoon / Thiyagalingam, Jeyan

2022

Abstract: Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, ... ...

Abstract	Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without regularizers. The proposed model is compared to seven state-of-the-art generative models based on autoencoders and evaluated based on five supervised disentanglement metrics. The experimental results show that the proposed model can have better disentanglement when variances of each features are different. We believe that this model leads to a new field for disentanglement learning based on autoencoders without regularizers. Comment: 8 Pages + 11 Page Append + References
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Publishing date	2022-02-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Zero Coordinate Shift

Leng, Kuangdai / Shankar, Mallikarjun / Thiyagalingam, Jeyan

Whetted Automatic Differentiation for Physics-informed Operator Learning

2023

Abstract: Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of collocation points. In this paper, we present a novel and lightweight ... ...

Abstract	Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of collocation points. In this paper, we present a novel and lightweight algorithm to conduct AD for physics-informed operator learning, which we call the trick of Zero Coordinate Shift (ZCS). Instead of making all sampled coordinates as leaf variables, ZCS introduces only one scalar-valued leaf variable for each spatial or temporal dimension, simplifying the wanted derivatives from "many-roots-many-leaves" to "one-root-many-leaves" whereby reverse-mode AD becomes directly utilisable. It has led to an outstanding performance leap by avoiding the duplication of the computational graph along the dimension of functions (physical parameters). ZCS is easy to implement with current deep learning libraries; our own implementation is achieved by extending the DeepXDE package. We carry out a comprehensive benchmark analysis and several case studies, training physics-informed DeepONets to solve partial differential equations (PDEs) without data. The results show that ZCS has persistently reduced GPU memory consumption and wall time for training by an order of magnitude, and such reduction factor scales with the number of functions. As a low-level optimisation technique, ZCS imposes no restrictions on data, physics (PDE) or network architecture and does not compromise training results from any aspect. Comment: 19 pages; this minor revision gives clearer explanation on the reason of performance boost by ZCS
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Mathematics - Numerical Analysis ; Physics - Computational Physics
Subject code	006
Publishing date	2023-11-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Multilane Tracking Algorithm Using IPDA with Intensity Feature.

Akbari, Behzad / Thiyagalingam, Jeyan / Lee, Richard / Thia, Kirubarajan

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 2

Abstract: Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and ... ...

Abstract	Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as 5%, 12%, and 3% improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers 57%, 31%, 4%, and 9× improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.
Language	English
Publishing date	2021-01-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21020461
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Entropy-based active learning of graph neural network surrogate models for materials properties.

Allotey, Johannes / Butler, Keith T / Thiyagalingam, Jeyan

The Journal of chemical physics

2021 Volume 155, Issue 17, Page(s) 174116

Abstract: Graph neural networks trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks once trained are able to make highly accurate predictions at a fraction of the cost of experiments ... ...

Abstract	Graph neural networks trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks once trained are able to make highly accurate predictions at a fraction of the cost of experiments or first-principles calculations of comparable accuracy. However, these networks typically rely on large databases of labeled experiments to train the model. In scenarios where data are scarce or expensive to obtain, this can be prohibitive. By building a neural network that provides confidence on the predicted properties, we are able to develop an active learning scheme that can reduce the amount of labeled data required by identifying the areas of chemical space where the model is most uncertain. We present a scheme for coupling a graph neural network with a Gaussian process to featurize solid-state materials and predict properties including a measure of confidence in the prediction. We then demonstrate that this scheme can be used in an active learning context to speed up the training of the model by selecting the optimal next experiment for obtaining a data label. Our active learning scheme can double the rate at which the performance of the model on a test dataset improves with additional data compared to choosing the next sample at random. This type of uncertainty quantification and active learning has the potential to open up new areas of materials science, where data are scarce and expensive to obtain, to the transformative power of graph neural networks.
Language	English
Publishing date	2021-11-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/5.0065694
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

Hatton, Daniel / Cha, Jaehoon / Riggs, Stephen / Harrison, Peter J / Thiyagalingam, Jeyan / Clare, Daniel K / Morris, Kyle L

Acta crystallographica. Section D, Structural biology

2024 Volume 80, Issue Pt 4, Page(s) 259–269

Abstract: The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant ...

Abstract	The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.
MeSH term(s)	Single Molecule Imaging ; Cryoelectron Microscopy ; Data Collection ; Software ; Specimen Handling
Language	English
Publishing date	2024-03-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2968623-4
ISSN	2059-7983 ; 0907-4449
ISSN (online)	2059-7983
ISSN	0907-4449
DOI	10.1107/S2059798324001578
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Article ; Online: A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks.

Zhang, Le / Thiyagalingam, Jeyan / Xue, Anke / Xu, Shuwen

Sensors (Basel, Switzerland)

2020 Volume 20, Issue 22

Abstract: Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter ... ...

Abstract	Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.
Language	English
Publishing date	2020-11-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s20226491
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Article ; Online: Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data.

Butler, Keith T / Le, Manh Duc / Thiyagalingam, Jeyan / Perring, Toby G

Journal of physics. Condensed matter : an Institute of Physics journal

2021 Volume 33, Issue 19

Abstract: Deep neural networks (NNs) provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and desired ... ...

Abstract	Deep neural networks (NNs) provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and desired property. In the context of inelastic neutron scattering experiments, however, as in many other scientific scenarios, a number of issues arise: (i) scarcity of labelled experimental data, (ii) lack of uncertainty quantification on results, and (iii) lack of interpretability of the deep NNs. In this work we examine approaches to all three issues. We use simulated data to train a deep NN to distinguish between two possible magnetic exchange models of a half-doped manganite. We apply the recently developed deterministic uncertainty quantification method to provide error estimates for the classification, demonstrating in the process how important realistic representations of instrument resolution in the training data are for reliable estimates on experimental data. Finally we use class activation maps to determine which regions of the spectra are most important for the final classification result reached by the network.
Language	English
Publishing date	2021-04-27
Publishing country	England
Document type	Journal Article
ZDB-ID	1472968-4
ISSN	1361-648X ; 0953-8984
ISSN (online)	1361-648X
ISSN	0953-8984
DOI	10.1088/1361-648X/abea1c
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Article ; Online: Assessment of protein-protein interfaces in cryo-EM derived assemblies.

Malhotra, Sony / Joseph, Agnel Praveen / Thiyagalingam, Jeyan / Topf, Maya

Nature communications

2021 Volume 12, Issue 1, Page(s) 3399

Abstract: Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no ... ...

Abstract	Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.
MeSH term(s)	Cryoelectron Microscopy/methods ; Humans ; Machine Learning ; Macromolecular Substances/chemistry ; Macromolecular Substances/metabolism ; Macromolecular Substances/ultrastructure ; Models, Molecular ; Neural Networks, Computer ; Protein Conformation ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping/methods ; Protein Interaction Maps ; Protein Multimerization ; Proteins/chemistry ; Proteins/metabolism ; Proteins/ultrastructure ; Support Vector Machine ; Viral Nonstructural Proteins/chemistry ; Viral Nonstructural Proteins/metabolism ; Viral Nonstructural Proteins/ultrastructure
Chemical Substances	Macromolecular Substances ; NSP1 protein, SARS-CoV-2 ; Proteins ; Viral Nonstructural Proteins
Language	English
Publishing date	2021-06-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-23692-x
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