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Learning where to see : a novel attention model for automated immunohistochemical scoring
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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
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Official URL: https://doi.org/10.1109/TMI.2019.2907049
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 | ||||||||
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| 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: |
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| 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 | ||||||||
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