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19 April 2024 |
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Article overview
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An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray | | Jingxin Liu
; Bolei Xu
; Chi Zheng
; Yuanhao Gong
; Jon Garibaldi
; Daniele Soria
; Andew Green
; Ian O. Ellis
; Wenbin Zou
; Guoping Qiu
; | | Date: |
19 Jan 2018 | | Abstract: | One of the methods for stratifying different molecular classes of breast
cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer
relevant biomarkers to stain tumour tissues prepared on tissue microarray
(TMA). To determine the molecular class of the tumour, pathologists will have
to manually mark the nuclei activity biomarkers through a microscope and use a
semi-quantitative assessment method to assign a histochemical score (H-Score)
to each TMA core. Manually marking positively stained nuclei is a time
consuming, imprecise and subjective process which will lead to inter-observer
and intra-observer discrepancies. In this paper, we present an end-to-end deep
learning system which directly predicts the H-Score automatically. Our system
imitates the pathologists’ decision process and uses one fully convolutional
network (FCN) to extract all nuclei region (tumour and non-tumour), a second
FCN to extract tumour nuclei region, and a multi-column convolutional neural
network which takes the outputs of the first two FCNs and the stain intensity
description image as input and acts as the high-level decision making mechanism
to directly output the H-Score of the input TMA image. To the best of our
knowledge, this is the first end-to-end system that takes a TMA image as input
and directly outputs a clinical score. We will present experimental results
which demonstrate that the H-Scores predicted by our model have very high and
statistically significant correlation with experienced pathologists’ scores and
that the H-Score discrepancy between our algorithm and the pathologists is on
par with the inter-subject discrepancy between the pathologists. | | Source: | arXiv, 1801.6288 | | Services: | Forum | Review | PDF | Favorites | |
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