ScanNet: A fast and dense scanning framework for metastastic breast cancer detection from whole-slide image
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years, computerized histology diagnosis has become one of the most rapidly expanding directions 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 challenging problem, due to the large-scale data with enormous resolutions and existence of hard mimics resulting in a large number of false positives. In this paper, we propose a novel framework by leveraging fully convolutional networks for efficient inference to meet the speed requirement for clinical practice, while reconstructing dense predictions under different offsets for ensuring accurate detection on both microand macro-metastases. Incorporating with the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge corroborated the efficacy of our method. Compared with the state-of-the-art methods, our method achieved superior performance with a faster speed on the tumor localization task and even surpassed human performance on the WSI classification task.
Date Issued
2018-05-07
Date Acceptance
2018-03-12
Citation
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp.539-546
ISBN
9781538648865
ISSN
2472-6737
Publisher
IEEE
Start Page
539
End Page
546
Journal / Book Title
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Copyright Statement
© 2018 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.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000434349200059&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
18th IEEE Winter Conference on Applications of Computer Vision (WACV)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
BAYESIAN BELIEF NETWORKS
HISTOLOGY IMAGES
HISTOPATHOLOGY IMAGES
MITOSIS DETECTION
SEGMENTATION
DIAGNOSIS
Publication Status
Published
Start Date
2018-03-12
Finish Date
2018-03-15
Coverage Spatial
Lake Tahoe, NV, USA
Date Publish Online
2018-05-07