Automated Molecular Subtyping of Breast Cancer Through Immunohistochemistry Image Analysis

S. Niyas, Shraddha Priya, Reena Oswal, Tojo Mathew, Jyoti R. Kini, Jeny Rajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Molecular subtyping has a significant role in cancer prognosis and targeted therapy. However, the prevalent manual procedure for this has disadvantages, such as deficit of medical experts, inter-observer variability, and high time consumption. This paper suggests a novel approach to automate molecular subtyping of breast cancer using an end-to-end deep learning model. Immunohistochemistry (IHC) images of the tumor tissues are analyzed using a three-stage system to determine the subtype. A modified Res-UNet CNN architecture is used in the first stage to segregate the biomarker responses. This is followed by using a CNN classifier to determine the status of the four biomarkers. Finally, the biomarker statuses are combined to determine the specific subtype of breast cancer. For each IHC biomarker, the performance of segmentation models is analyzed qualitatively and quantitatively. In addition, the patient-level biomarker prediction results are also assessed. The findings of the suggested technique demonstrate the potential of computer-aided techniques to diagnose the subtypes of breast cancer. The proposed automated molecular subtyping approach can accelerate pathology procedures, considerably reduce pathologists’ workload, and minimize the overall cost and time required for diagnosis and treatment planning.

Original languageEnglish
Title of host publicationComputer Vision and Machine Intelligence - Proceedings of CVMI 2022
EditorsMassimo Tistarelli, Shiv Ram Dubey, Satish Kumar Singh, Xiaoyi Jiang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789811978661
Publication statusPublished - 2023
EventInternational Conference on Computer Vision and Machine Intelligence, CVMI 2022 - Prayagraj, India
Duration: 12-08-202213-08-2022

Publication series

NameLecture Notes in Networks and Systems
Volume586 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Computer Vision and Machine Intelligence, CVMI 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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