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Razavian, Ali Sharif
Publications (3 of 3) Show all publications
Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., . . . Gordon, M. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures?. Acta Orthopaedica, 88(6), 581-586
Open this publication in new window or tab >>Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures?
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2017 (English)In: Acta Orthopaedica, ISSN 1745-3674, E-ISSN 1745-3682, Vol. 88, no 6, p. 581-586Article in journal (Refereed) Published
Abstract [en]

Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

National Category
Orthopaedics
Identifiers
urn:nbn:se:kth:diva-220304 (URN)10.1080/17453674.2017.1344459 (DOI)000416605900005 ()28681679 (PubMedID)2-s2.0-85021907834 (Scopus ID)
Note

QC 20171221

Available from: 2017-12-21 Created: 2017-12-21 Last updated: 2020-03-09Bibliographically 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
Razavian, A. S., Sullivan, J., Carlsson, S. & Maki, A. (2016). Visual instance retrieval with deep convolutional networks. ITE Transactions on Media Technology and Applications, 4(3), 251-258
Open this publication in new window or tab >>Visual instance retrieval with deep convolutional networks
2016 (English)In: ITE Transactions on Media Technology and Applications, ISSN 2186-7364, Vol. 4, no 3, p. 251-258Article in journal (Refereed) Published
Abstract [en]

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately.

Place, publisher, year, edition, pages
Institute of Image Information and Television Engineers, 2016
Keywords
Convolutional network, Learning representation, Multi-resolution search, Visual instance retrieval
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-195472 (URN)10.3169/mta.4.251 (DOI)2-s2.0-84979503481 (Scopus ID)
Note

QC 20161125

Available from: 2016-11-25 Created: 2016-11-03 Last updated: 2020-03-05Bibliographically approved
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