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Publications (10 of 19) Show all publications
Dembrower, K., Liu, Y., Azizpour, H., Eklund, M., Smith, K., Lindholm, P. & Strand, F. (2020). Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology, 294(2), 265-272
Open this publication in new window or tab >>Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction
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2020 (English)In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 294, no 2, p. 265-272Article in journal (Refereed) Published
Abstract [en]

Background: Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose: To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods: In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results: A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P< .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P< .001), and the percentage density (mean, 25.6% vs 24.0%; P< .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P< .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P< .001); this difference was most pronounced for more aggressive cancers. Conclusion: Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.

Place, publisher, year, edition, pages
Radiological Society of North America Inc., 2020
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-267834 (URN)10.1148/radiol.2019190872 (DOI)000508455500006 ()31845842 (PubMedID)2-s2.0-85078538925 (Scopus ID)
Note

QC 20200227

Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-02-27Bibliographically approved
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., . . . Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), Article ID 233.
Open this publication in new window or tab >>The role of artificial intelligence in achieving the Sustainable Development Goals
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2020 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 11, no 1, article id 233Article, review/survey (Refereed) Published
Abstract [en]

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

Place, publisher, year, edition, pages
Nature Research, 2020
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:kth:diva-267774 (URN)10.1038/s41467-019-14108-y (DOI)000511916800011 ()31932590 (PubMedID)2-s2.0-85077785900 (Scopus ID)
Note

QC 20200302

Available from: 2020-03-02 Created: 2020-03-02 Last updated: 2020-03-04
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., . . . Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), Article ID 233.
Open this publication in new window or tab >>The role of artificial intelligence in achieving the Sustainable Development Goals
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2020 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 11, no 1, article id 233Article, review/survey (Refereed) Published
Abstract [en]

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2020
National Category
Robotics Environmental Sciences
Identifiers
urn:nbn:se:kth:diva-269019 (URN)10.1038/s41467-019-14108-y (DOI)000511916800011 ()31932590 (PubMedID)2-s2.0-85077785900 (Scopus ID)
Note

QC 20200316

Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-03-16Bibliographically approved
Englesson, E. & Azizpour, H. (2019). Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation. In: : . Paper presented at International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Uncertainty and Robustness in Deep Learning.
Open this publication in new window or tab >>Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-260511 (URN)
Conference
International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Uncertainty and Robustness in Deep Learning
Note

QC 20191001

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Baldassarre, F. & Azizpour, H. (2019). Explainability Techniques for Graph Convolutional Networks. In: : . Paper presented at International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations.
Open this publication in new window or tab >>Explainability Techniques for Graph Convolutional Networks
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Graph Networks are used to make decisions in potentially complex scenarios but it is usually not obvious how or why they made them. In this work, we study the explainability of Graph Network decisions using two main classes of techniques, gradient-based and decomposition-based, on a toy dataset and a chemistry task. Our study sets the ground for future development as well as application to real-world problems.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-260507 (URN)
Conference
International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations
Note

QC 20191001

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Srinivasan, P. A., Guastoni, L., Azizpour, H., Schlatter, P. & Vinuesa, R. (2019). Predictions of turbulent shear flows using deep neural networks. Physical Review Fluids, 4(5), Article ID 054603.
Open this publication in new window or tab >>Predictions of turbulent shear flows using deep neural networks
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2019 (English)In: Physical Review Fluids, E-ISSN 2469-990X, Vol. 4, no 5, article id 054603Article in journal (Refereed) Published
Abstract [en]

In the present work, we assess the capabilities of neural networks to predict temporally evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehlis et al. [New J. Phys. 6, 56 (2004)] to generate training data for two types of neural networks: the multilayer perceptron (MLP) and the long short-term memory (LSTM) networks. We tested a number of neural network architectures by varying the number of layers, number of units per layer, dimension of the input, and weight initialization and activation functions in order to obtain the best configurations for flow prediction. Because of its ability to exploit the sequential nature of the data, the LSTM network outperformed the MLP. The LSTM led to excellent predictions of turbulence statistics (with relative errors of 0.45% and 2.49% in mean and fluctuating quantities, respectively) and of the dynamical behavior of the system (characterized by Poincare maps and Lyapunov exponents). This is an exploratory study where we consider a low-order representation of near-wall turbulence. Based on the present results, the proposed machine-learning framework may underpin future applications aimed at developing accurate and efficient data-driven subgrid-scale models for large-eddy simulations of more complex wall-bounded turbulent flows, including channels and developing boundary layers.

Place, publisher, year, edition, pages
AMER PHYSICAL SOC, 2019
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-252606 (URN)10.1103/PhysRevFluids.4.054603 (DOI)000467744500004 ()2-s2.0-85067117820 (Scopus ID)
Note

QC 20190610

Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2020-03-09Bibliographically approved
Robertson, S., Azizpour, H., Smith, K. & Hartman, J. (2018). Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Translational Research: The Journal of Laboratory and Clinical Medicine, 194, 19-35
Open this publication in new window or tab >>Digital image analysis in breast pathology-from image processing techniques to artificial intelligence
2018 (English)In: Translational Research: The Journal of Laboratory and Clinical Medicine, ISSN 1931-5244, E-ISSN 1878-1810, Vol. 194, p. 19-35Article, review/survey (Refereed) Published
Abstract [en]

Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2018
National Category
Cancer and Oncology Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-226196 (URN)10.1016/j.trsl.2017.10.010 (DOI)000428608600002 ()29175265 (PubMedID)2-s2.0-85036635168 (Scopus ID)
Note

QC 20180518

Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2019-09-18Bibliographically approved
Smith, K., Piccinini, F., Balassa, T., Koos, K., Danka, T., Azizpour, H. & Horvath, P. (2018). Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays. CELL SYSTEMS, 6(6), 636-653
Open this publication in new window or tab >>Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
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2018 (English)In: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, no 6, p. 636-653Article, review/survey (Refereed) Published
Abstract [en]

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-232249 (URN)10.1016/j.cels.2018.06.001 (DOI)000436877800002 ()29953863 (PubMedID)2-s2.0-85048445198 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180720

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2019-09-17Bibliographically approved
Carlsson, S., Azizpour, H., Razavian, A. S., Sullivan, J. & Smith, K. (2017). The Preimage of Rectifier Network Activities. In: International Conference on Learning Representations (ICLR): . Paper presented at International Conference on Learning Representations (ICLR).
Open this publication in new window or tab >>The Preimage of Rectifier Network Activities
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2017 (English)In: International Conference on Learning Representations (ICLR), 2017Conference paper, Published paper (Refereed)
Abstract [en]

The preimage of the activity at a certain level of a deep network is the set of inputs that result in the same node activity. For fully connected multi layer rectifier networks we demonstrate how to compute the preimages of activities at arbitrary levels from knowledge of the parameters in a deep rectifying network. If the preimage set of a certain activity in the network contains elements from more than one class it means that these classes are irreversibly mixed. This implies that preimage sets which are piecewise linear manifolds are building blocks for describing the input manifolds specific classes, ie all preimages should ideally be from the same class. We believe that the knowledge of how to compute preimages will be valuable in understanding the efficiency displayed by deep learning networks and could potentially be used in designing more efficient training algorithms.

National Category
Computer Vision and Robotics (Autonomous Systems) Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-259164 (URN)2-s2.0-85071123889 (Scopus ID)
Conference
International Conference on Learning Representations (ICLR)
Note

QC 20190916

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-16Bibliographically approved
Azizpour, H., Sharif Razavian, A., Sullivan, J., Maki, A. & Carlssom, S. (2016). Factors of Transferability for a Generic ConvNet Representation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 38(9), 1790-1802, Article ID 7328311.
Open this publication in new window or tab >>Factors of Transferability for a Generic ConvNet Representation
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2016 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 38, no 9, p. 1790-1802, article id 7328311Article in journal (Refereed) Published
Abstract [en]

Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their similarity to the source task such that a correlation between the performance of tasks and their similarity to the source task w.r.t. the proposed factors is observed.

Place, publisher, year, edition, pages
IEEE Computer Society Digital Library, 2016
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-177033 (URN)10.1109/TPAMI.2015.2500224 (DOI)000381432700006 ()2-s2.0-84981266620 (Scopus ID)
Note

QC 20161208

Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5211-6388

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