Ki67 proliferation index quantification using silver standard masks

Seyed Hossein Mirjahanmardi; Melanie Dawe; Anthony Fyles; Wei Shi; Dimitri Androutsos; Fei-Fei Liu; Susan Done; April Khademi

doi:10.1117/12.2654599

6 April 2023 Ki67 proliferation index quantification using silver standard masks

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Dimitri Androutsos, Fei-Fei Liu, Susan Done, April Khademi

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710L (2023) https://doi.org/10.1117/12.2654599
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

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Citation Download Citation

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Dimitri Androutsos, Fei-Fei Liu, Susan Done, and April Khademi "Ki67 proliferation index quantification using silver standard masks", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710L (6 April 2023); https://doi.org/10.1117/12.2654599

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