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Publikationer (10 of 13) Visa alla publikationer
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.
Öppna denna publikation i ny flik eller fönster >>Predictions of turbulent shear flows using deep neural networks
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2019 (Engelska)Ingår i: Physical Review Fluids, E-ISSN 2469-990X, Vol. 4, nr 5, artikel-id 054603Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
AMER PHYSICAL SOC, 2019
Nationell ämneskategori
Fysik
Identifikatorer
urn:nbn:se:kth:diva-252606 (URN)10.1103/PhysRevFluids.4.054603 (DOI)000467744500004 ()
Anmärkning

QC 20190610

Tillgänglig från: 2019-06-10 Skapad: 2019-06-10 Senast uppdaterad: 2019-06-10Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Digital image analysis in breast pathology-from image processing techniques to artificial intelligence
2018 (Engelska)Ingår i: Translational Research: The Journal of Laboratory and Clinical Medicine, ISSN 1931-5244, E-ISSN 1878-1810, Vol. 194, s. 19-35Artikel, forskningsöversikt (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
ELSEVIER SCIENCE INC, 2018
Nationell ämneskategori
Cancer och onkologi Radiologi och bildbehandling
Identifikatorer
urn:nbn:se:kth:diva-226196 (URN)10.1016/j.trsl.2017.10.010 (DOI)000428608600002 ()29175265 (PubMedID)2-s2.0-85036635168 (Scopus ID)
Anmärkning

QC 20180518

Tillgänglig från: 2018-05-18 Skapad: 2018-05-18 Senast uppdaterad: 2018-05-18Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
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2018 (Engelska)Ingår i: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, nr 6, s. 636-653Artikel, forskningsöversikt (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
CELL PRESS, 2018
Nationell ämneskategori
Bioinformatik och systembiologi
Identifikatorer
urn:nbn:se:kth:diva-232249 (URN)10.1016/j.cels.2018.06.001 (DOI)000436877800002 ()29953863 (PubMedID)2-s2.0-85048445198 (Scopus ID)
Forskningsfinansiär
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Anmärkning

QC 20180720

Tillgänglig från: 2018-07-20 Skapad: 2018-07-20 Senast uppdaterad: 2018-07-20Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Factors of Transferability for a Generic ConvNet Representation
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2016 (Engelska)Ingår i: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 38, nr 9, s. 1790-1802, artikel-id 7328311Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society Digital Library, 2016
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Forskningsämne
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-177033 (URN)10.1109/TPAMI.2015.2500224 (DOI)000381432700006 ()2-s2.0-84981266620 (Scopus ID)
Anmärkning

QC 20161208

Tillgänglig från: 2015-11-13 Skapad: 2015-11-13 Senast uppdaterad: 2018-01-10Bibliografiskt granskad
Azizpour, H. (2016). Visual Representations and Models: From Latent SVM to Deep Learning. (Doctoral dissertation). Stockholm, Sweden: KTH Royal Institute of Technology
Öppna denna publikation i ny flik eller fönster >>Visual Representations and Models: From Latent SVM to Deep Learning
2016 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning.

First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sampled space of the positive distribution is usually structured. The proposed clustering techniques include a novel similarity measure based on exemplar learning, an approach for using additional annotation, and augmenting latent SVM to automatically find clusters whose members can be reliably distinguished from background class. 

In another effort, a strongly supervised DPM is suggested to study how these models can benefit from privileged information. The extra information comes in the form of semantic parts annotation (i.e. their presence and location). And they are used to constrain DPMs latent variables during or prior to the optimization of the latent SVM. Its effectiveness is demonstrated on the task of animal detection.

Finally, we generalize the formulation of discriminative latent variable models, including DPMs, to incorporate new set of latent variables representing the structure or properties of negative samples. Thus, we term them as negative latent variables. We show this generalization affects state-of-the-art techniques and helps the visual recognition by explicitly searching for counter evidences of an object presence.

Following the resurgence of deep networks, in the last works of this thesis we have focused on deep learning in order to produce a generic representation for visual recognition. A Convolutional Network (ConvNet) is trained on a largely annotated image classification dataset called ImageNet with $\sim1.3$ million images. Then, the activations at each layer of the trained ConvNet can be treated as the representation of an input image. We show that such a representation is surprisingly effective for various recognition tasks, making it clearly superior to all the handcrafted features previously used in visual recognition (such as HOG in our first works on DPM). We further investigate the ways that one can improve this representation for a task in mind. We propose various factors involving before or after the training of the representation which can improve the efficacy of the ConvNet representation. These factors are analyzed on 16 datasets from various subfields of visual recognition.

Ort, förlag, år, upplaga, sidor
Stockholm, Sweden: KTH Royal Institute of Technology, 2016. s. 172
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 21
Nyckelord
Computer Vision, Machine Learning, Artificial Intelligence, Deep Learning, Learning Representation, Deformable Part Models, Discriminative Latent Variable Models, Convolutional Networks, Object Recognition, Object Detection
Nationell ämneskategori
Elektroteknik och elektronik Datorsystem
Forskningsämne
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-192289 (URN)978-91-7729-110-7 (ISBN)
Externt samarbete:
Disputation
2016-09-27, Kollegiesalen, Brinellvägen 8, KTH-huset, våningsplan 4, KTH Campus, Stockholm, 15:26 (Engelska)
Opponent
Handledare
Anmärkning

QC 20160908

Tillgänglig från: 2016-09-08 Skapad: 2016-09-08 Senast uppdaterad: 2016-09-09Bibliografiskt granskad
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A. & Carlsson, S. (2015). From Generic to Specific Deep Representations for Visual Recognition. In: Proceedings of CVPR 2015: . Paper presented at CVPRW DeepVision Workshop,June 11, 2015, Boston, MA, USA. IEEE conference proceedings
Öppna denna publikation i ny flik eller fönster >>From Generic to Specific Deep Representations for Visual Recognition
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2015 (Engelska)Ingår i: Proceedings of CVPR 2015, IEEE conference proceedings, 2015Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Evidence is mounting that ConvNets are the best representation learning method for recognition. In the common scenario, a ConvNet is trained on a large labeled dataset and the feed-forward units activation, at a certain layer of the network, is used as a generic representation of an input image. Recent studies have shown this form of representation to be astoundingly effective for a wide range of recognition tasks. This paper thoroughly investigates the transferability of such representations w.r.t. several factors. It includes parameters for training the network such as its architecture and parameters of feature extraction. We further show that different visual recognition tasks can be categorically ordered based on their distance from the source task. We then show interesting results indicating a clear correlation between the performance of tasks and their distance from the source task conditioned on proposed factors. Furthermore, by optimizing these factors, we achieve stateof-the-art performances on 16 visual recognition tasks.

Ort, förlag, år, upplaga, sidor
IEEE conference proceedings, 2015
Serie
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:kth:diva-164527 (URN)10.1109/CVPRW.2015.7301270 (DOI)000378887900005 ()2-s2.0-84951960494 (Scopus ID)978-146736759-2 (ISBN)
Konferens
CVPRW DeepVision Workshop,June 11, 2015, Boston, MA, USA
Anmärkning

QC 20150507

Tillgänglig från: 2015-04-17 Skapad: 2015-04-17 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Sharif Razavian, A., Azizpour, H., Maki, A., Sullivan, J., Ek, C. H. & Carlsson, S. (2015). Persistent Evidence of Local Image Properties in Generic ConvNets. In: Paulsen, Rasmus R., Pedersen, Kim S. (Ed.), Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings. Paper presented at Scandinavian Conference on Image Analysis, Copenhagen, Denmark, 15-17 June, 2015 (pp. 249-262). Springer Publishing Company
Öppna denna publikation i ny flik eller fönster >>Persistent Evidence of Local Image Properties in Generic ConvNets
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2015 (Engelska)Ingår i: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Paulsen, Rasmus R., Pedersen, Kim S., Springer Publishing Company, 2015, s. 249-262Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or thevariation within the object class. Does this happen in practice? Although this seems to pertain to the very final layers in the network, if we look at earlier layers we find that this is not the case. Surprisingly, strong spatial information is implicit. This paper addresses this, in particular, exploiting the image representation at the first fully connected layer,i.e. the global image descriptor which has been recently shown to be most effective in a range of visual recognition tasks. We empirically demonstrate evidences for the finding in the contexts of four different tasks: 2d landmark detection, 2d object keypoints prediction, estimation of the RGB values of input image, and recovery of semantic label of each pixel. We base our investigation on a simple framework with ridge rigression commonly across these tasks,and show results which all support our insight. Such spatial information can be used for computing correspondence of landmarks to a good accuracy, but should potentially be useful for improving the training of the convolutional nets for classification purposes.

Ort, förlag, år, upplaga, sidor
Springer Publishing Company, 2015
Serie
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 9127
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:kth:diva-172140 (URN)10.1007/978-3-319-19665-7_21 (DOI)2-s2.0-84947982864 (Scopus ID)
Konferens
Scandinavian Conference on Image Analysis, Copenhagen, Denmark, 15-17 June, 2015
Anmärkning

Qc 20150828

Tillgänglig från: 2015-08-13 Skapad: 2015-08-13 Senast uppdaterad: 2016-12-09Bibliografiskt granskad
Azizpour, H., Arefiyan, M., Naderi Parizi, S. & Carlsson, S. (2015). Spotlight the Negatives: A Generalized Discriminative Latent Model. In: : . Paper presented at British Machine Vision Conference (BMVC),7-10 September, Swansea, UK, 2015.
Öppna denna publikation i ny flik eller fönster >>Spotlight the Negatives: A Generalized Discriminative Latent Model
2015 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Discriminative latent variable models (LVM) are frequently applied to various visualrecognition tasks. In these systems the latent (hidden) variables provide a formalism formodeling structured variation of visual features. Conventionally, latent variables are de-fined on the variation of the foreground (positive) class. In this work we augment LVMsto includenegativelatent variables corresponding to the background class. We formalizethe scoring function of such a generalized LVM (GLVM). Then we discuss a frameworkfor learning a model based on the GLVM scoring function. We theoretically showcasehow some of the current visual recognition methods can benefit from this generalization.Finally, we experiment on a generalized form of Deformable Part Models with negativelatent variables and show significant improvements on two different detection tasks.

Nationell ämneskategori
Datorsystem
Forskningsämne
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-172138 (URN)
Konferens
British Machine Vision Conference (BMVC),7-10 September, Swansea, UK, 2015
Anmärkning

QC 20150828

Tillgänglig från: 2015-08-13 Skapad: 2015-08-13 Senast uppdaterad: 2016-09-08Bibliografiskt granskad
Sharif Razavian, A., Azizpour, H., Sullivan, J. & Carlsson, S. (2014). CNN features off-the-shelf: An Astounding Baseline for Recognition. In: Proceedings of CVPR 2014: . Paper presented at Computer Vision and Pattern Recognition (CVPR) 2014, DeepVision workshop,June 28, 2014, Columbus, Ohio.
Öppna denna publikation i ny flik eller fönster >>CNN features off-the-shelf: An Astounding Baseline for Recognition
2014 (Engelska)Ingår i: Proceedings of CVPR 2014, 2014Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the OverFeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or L2 distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.

Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:kth:diva-149178 (URN)10.1109/CVPRW.2014.131 (DOI)000349552300079 ()2-s2.0-84908537903 (Scopus ID)
Konferens
Computer Vision and Pattern Recognition (CVPR) 2014, DeepVision workshop,June 28, 2014, Columbus, Ohio
Anmärkning

Best Paper Runner-up Award.

QC 20140825

Tillgänglig från: 2014-08-16 Skapad: 2014-08-16 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Kazemi, V., Burenius, M., Azizpour, H. & Sullivan, J. (2013). Multi-view body part recognition with random forests. In: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013: . Paper presented at 2013 24th British Machine Vision Conference, BMVC 2013; Bristol; United Kingdom; 9 September 2013 through 13 September 2013. Bristol, England: British Machine Vision Association
Öppna denna publikation i ny flik eller fönster >>Multi-view body part recognition with random forests
2013 (Engelska)Ingår i: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, Bristol, England: British Machine Vision Association , 2013Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classifier to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game.

Ort, förlag, år, upplaga, sidor
Bristol, England: British Machine Vision Association, 2013
Nyckelord
Data processing, Decision trees, Motion estimation, Body part recognition, Calibrated cameras, Football game, Human pose estimations, Latent variable, Part-based models, Random forest classifier, Random forests
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:kth:diva-134190 (URN)10.5244/C.27.48 (DOI)000346352700045 ()2-s2.0-84898413079 (Scopus ID)
Konferens
2013 24th British Machine Vision Conference, BMVC 2013; Bristol; United Kingdom; 9 September 2013 through 13 September 2013
Forskningsfinansiär
EU, FP7, Sjunde ramprogrammet
Anmärkning

QC 20131217

Tillgänglig från: 2013-11-19 Skapad: 2013-11-19 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-5211-6388

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