Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Learning where to see : a novel attention model for automated immunohistochemical scoring

Tools
- Tools
+ Tools

Qaiser, Talha and Rajpoot, Nasir M. (Nasir Mahmood) (2019) Learning where to see : a novel attention model for automated immunohistochemical scoring. IEEE Transactions on Medical Imaging, 38 (11). pp. 2620-2631. doi:10.1109/TMI.2019.2907049

[img]
Preview
PDF
WRAP-learning-where-see-novel-attention-model-immunohistochemical-Rajpoot-2019.pdf - Accepted Version - Requires a PDF viewer.

Download (5Mb) | Preview
Official URL: https://doi.org/10.1109/TMI.2019.2907049

Request Changes to record.

Abstract

Estimatingover-amplification of human epidermal growth factor receptor2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the subimage patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model out performs other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Cancer -- Histopathology, Diagnostic imaging
Journal or Publication Title: IEEE Transactions on Medical Imaging
Publisher: IEEE
ISSN: 0278-0062
Official Date: November 2019
Dates:
DateEvent
November 2019Published
22 March 2019Available
13 March 2019Accepted
Volume: 38
Number: 11
Page Range: pp. 2620-2631
DOI: 10.1109/TMI.2019.2907049
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: © 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
Related URLs:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us