Abstract
In an effort to ease the job of pathologists while examining Hematoxylin and Eosin stained breast tissue, this study presents a deep learning-based classifier of nuclear pleomorphism according to the Nottingham grading scale. We show that high classification accuracy is attainable without pre-segmenting the cell nuclei. The data used in the experiments was acquired from our partner teaching hospital. It consists of image patches that were extracted from whole slide images. Using the labeled data, we compared the performance of three state-of-the-art convolutional neural networks and tested the trained model on the unseen testing data. Our experiments revealed that the densely connected architecture (DenseNet) outperforms the residual network (ResNet) and the dual path networks (DPN) in terms of accuracy and F1 score. Specifically, we reached an overall validation accuracy and F1 score of over 0.96 and 0.94 respectively.
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Iheme, L.O. et al. (2021). Patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_18
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