Deep Barcodes for Fast Retrieval of Histopathology Scans

Meghana Dinesh Kumar; Morteza Babaie; H. R. Tizhoosh

doi:10.1109/IJCNN.2018.8489574

Deep Barcodes for Fast Retrieval of Histopathology Scans

Meghana Dinesh Kumar, Morteza Babaie, H. R. Tizhoosh

Artificial Intelligence and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images. Since binary search is computationally less expensive, in terms of both speed and storage, deep bar-codes could be useful when dealing with big data retrieval. Our experiments use the dataset Kimia Path24 to test three pre-trained networks for image retrieval. The dataset consists of 27,055 training images in 24 different classes with large variability, and 1,325 test images for testing. Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.

Original language	English (US)
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489574
State	Published - Oct 10 2018
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: Jul 8 2018 → Jul 13 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	7/8/18 → 7/13/18

Keywords

Convolutional Neural Networks
deep barcodes
deep learning
digital pathology
image classification
image retrieval
medical imaging
transform learning.

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2018.8489574

Cite this

Kumar, M. D., Babaie, M., & Tizhoosh, H. R. (2018). Deep Barcodes for Fast Retrieval of Histopathology Scans. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489574 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489574

Deep Barcodes for Fast Retrieval of Histopathology Scans. / Kumar, Meghana Dinesh; Babaie, Morteza; Tizhoosh, H. R.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489574 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kumar, MD, Babaie, M & Tizhoosh, HR 2018, Deep Barcodes for Fast Retrieval of Histopathology Scans. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489574, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 7/8/18. https://doi.org/10.1109/IJCNN.2018.8489574

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abstract = "We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images. Since binary search is computationally less expensive, in terms of both speed and storage, deep bar-codes could be useful when dealing with big data retrieval. Our experiments use the dataset Kimia Path24 to test three pre-trained networks for image retrieval. The dataset consists of 27,055 training images in 24 different classes with large variability, and 1,325 test images for testing. Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.",

keywords = "Convolutional Neural Networks, deep barcodes, deep learning, digital pathology, image classification, image retrieval, medical imaging, transform learning.",

author = "Kumar, {Meghana Dinesh} and Morteza Babaie and Tizhoosh, {H. R.}",

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AB - We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images. Since binary search is computationally less expensive, in terms of both speed and storage, deep bar-codes could be useful when dealing with big data retrieval. Our experiments use the dataset Kimia Path24 to test three pre-trained networks for image retrieval. The dataset consists of 27,055 training images in 24 different classes with large variability, and 1,325 test images for testing. Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.

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Deep Barcodes for Fast Retrieval of Histopathology Scans

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Keywords

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