Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous).
Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Where traditional machine learning techniques fails.
Many models has been developed to classify digital pathology images where our customized model makes use of existing models in model ensemble approach and some skip connection in parallel to CNN model which improves performance by 3-7%.