Automated Molecular Subtyping of Breast Carcinoma using Deep Learning Techniques

S. Niyas, Ramya Bygari, Rachita Naik, Bhavishya Viswanath, Dhananjay Ugwekar, Tojo Mathew, J. Kavya, Jyoti R. Kini, Jeny Rajan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Objective: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images. Methods and procedures: A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker. Results: The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists. Clinical impact: Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists’ workload and associated costs, and facilitate targeted treatment to obtain better outcomes.

Original languageEnglish
Pages (from-to)161-169
JournalIEEE Journal of Translational Engineering in Health and Medicine
Publication statusPublished - 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


Dive into the research topics of 'Automated Molecular Subtyping of Breast Carcinoma using Deep Learning Techniques'. Together they form a unique fingerprint.

Cite this