Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading AI Driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI
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Abstract
Breast cancer is the second largest cancer caused in the world due to the uncontrollable growth in breast cells. Nottingham Grading is the internationally acceptable system to grade breast cancer. Nuclear pleomorphism is one of the breast cancer biomarkers for computing Nottingham grading. Pathologists grade nuclear pleomorphism on breast cancer glass tissue slides using a conventional microscope which is time consuming and has considerable inter-observer variability between pathologists. The paper proposed an Artificial Intelligence (AI) deep learning model to grade grade1, grade2, grade3 nuclear pleomorphism on breast cancer whole slide images (WSI). The proposed Yolov5 model is trained and tested on 1,30,000 WSI tiles having around two lakh annotations. The accuracy of the model is mAP 0.89. The proposed model saves the time and reduces the workload of the pathologist and also helps them to produce accurate results.
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