Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

• URL: http://arxiv.org/abs/2305.03546v2
• Date: Fri, 22 Sep 2023 08:14:42 GMT
• Title: Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
• Authors: Chuang Zhu, Shengjie Liu, Zekuan Yu, Feng Xu, Arpit Aggarwal, Germ\'an Corredor, Anant Madabhushi, Qixun Qu, Hongwei Fan, Fangda Li, Yueheng Li, Xianchao Guan, Yongbing Zhang, Vivek Kumar Singh, Farhan Akram, Md. Mostafa Kamal Sarker, Zhongyue Shi, Mulan Jin
• Abstract summary: We held the breast cancerchemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations.
• Score: 17.649693088941508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.

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