KEYWORDS: Education and training, Performance modeling, Breast cancer, Data modeling, Silver, Deep learning, Tunable filters, Tumors, Pathology, Evolutionary algorithms
Deep learning (DL) systems obtain high accuracy on digital pathology datasets that are within the same distribution as the training set. When applied to unseen datasets, performance degradation occurs due to differences in acquisition hardware/software and staining protocols/vendors. This issue poses a barrier to translation since developed models cannot be readily deployed at new labs. To overcome this challenge, we present silver standard (SS) annotations as a method to improve the performance of deep learning architectures on unseen Ki67 pathology images. An unsupervised technique referred to as IHCCH was used to generate SS masks for Ki67+ and Ki67− nuclei from the target lab. A previously validated architecture for Ki67, UV-Net, is trained with a combination of the gold standard (GS) and SS masks to enhance performance consistency. It was found that adding SS masks from the unseen center to the training pool improved performance over clinically relevant PI ranges.
Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images. Deep learning algorithms have shown strong potential in nuclei detection tasks. However, they face challenges when applied to pathology images with dense medium and overlapping nuclei since _ne details are often diluted or completely lost by early maxpooling layers. This paper introduces an optimized UV-Net architecture, specifically developed to recover nuclear details with high-resolution through feature preservation for Ki67 proliferation index computation. UV-Net achieves an average F1-score of 0.83 on held-out test patch data, while other architectures obtain 0.74- 0.79. On tissue microarrays (unseen) test data obtained from multiple centers, UV-Net's accuracy exceeds other architectures by a wide margin, including 9-42% on Ontario Veterinary College, 7-35% on Protein Atlas and 0.3-3% on University Health Network.
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