Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
| Autor: | Mulan Jin, Zhongyue Shi, Md. Mostafa Kamal Sarker, Farhan Akram, Vivek Kumar Singh, Yongbing Zhang, Xianchao Guan, Yueheng Li, Fangda Li, Hongwei Fan, Qixun Qu, Anant Madabhushi, Germán Corredor, Arpit Aggarwal, Zekuan Yu, Feng Xu, Shengjie Liu, Chuang Zhu |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing |
| Popis: | 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. 13 pages, 11 figures, 2tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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