TY - GEN
T1 - Automated Molecular Subtyping of Breast Cancer Through Immunohistochemistry Image Analysis
AU - Niyas, S.
AU - Priya, Shraddha
AU - Oswal, Reena
AU - Mathew, Tojo
AU - Kini, Jyoti R.
AU - Rajan, Jeny
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85161585656&partnerID=8YFLogxK
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U2 - 10.1007/978-981-19-7867-8_3
DO - 10.1007/978-981-19-7867-8_3
M3 - Conference contribution
AN - SCOPUS:85161585656
SN - 9789811978661
T3 - Lecture Notes in Networks and Systems
SP - 23
EP - 35
BT - Computer Vision and Machine Intelligence - Proceedings of CVMI 2022
A2 - Tistarelli, Massimo
A2 - Dubey, Shiv Ram
A2 - Singh, Satish Kumar
A2 - Jiang, Xiaoyi
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Computer Vision and Machine Intelligence, CVMI 2022
Y2 - 12 August 2022 through 13 August 2022
ER -
