![]() The results obtained by our method are highly in concordance with manual assessment by pathologists.Īccurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists' workload and associated costs, and facilitate targeted treatment to obtain better outcomes.Ĭlassifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. Furthermore, the biomarker prediction results are also evaluated. ![]() ![]() The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. 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. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. 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. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images.Ī modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. 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. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women.
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