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Fast ScanNet : fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection
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Lin, Huangjing , Chen, Hao , Graham, Simon, Dou, Qi , Rajpoot, Nasir M. (Nasir Mahmood) and Heng, Pheng-Ann (2019) Fast ScanNet : fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection. IEEE Transactions on Medical Imaging, 38 (8). pp. 1948-1958. doi:10.1109/TMI.2019.2891305 ISSN 0278-0062.
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WRAP-fast-ScanNet-dense-analysis-gigapixel-images-cancer-Rajpoot-2019.pdf - Accepted Version - Requires a PDF viewer. Download (19Mb) | Preview |
Official URL: https://doi.org/10.1109/TMI.2019.2891305
Abstract
Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole- slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists’ workload and simultaneously reduce misdiagnosis rate. However, automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice, but also densely scans the whole- slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method are corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumour localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | R Medicine > RC Internal medicine | ||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Metastasis -- Diagnosis, Lymphatic metastasis, Diagnostic imaging, Breast -- Histology | ||||||||||||
| Journal or Publication Title: | IEEE Transactions on Medical Imaging | ||||||||||||
| Publisher: | IEEE | ||||||||||||
| ISSN: | 0278-0062 | ||||||||||||
| Official Date: | August 2019 | ||||||||||||
| Dates: |
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| Volume: | 38 | ||||||||||||
| Number: | 8 | ||||||||||||
| Page Range: | pp. 1948-1958 | ||||||||||||
| DOI: | 10.1109/TMI.2019.2891305 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Date of first compliant deposit: | 7 January 2019 | ||||||||||||
| Date of first compliant Open Access: | 7 January 2019 | ||||||||||||
| RIOXX Funder/Project Grant: |
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