In Biomedizinische Technik. Biomedical engineering
OBJECTIVES : The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.
METHODS : In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.
RESULT : To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.
CONCLUSIONS : The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.
Ramamurthy Karthik, Varikuti Abinash Reddy, Gupta Bhavya, Aswani Nehal
2022-Nov-04
EfficientNet, Gleason grading, attention, kappa, prostate cancer
