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Papers/BCI: Breast Cancer Immunohistochemical Image Generation th...

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

ShengJie Liu, Chuang Zhu, Feng Xu, Xinyu Jia, Zhongyue Shi, Mulan Jin

2022-04-25Breast Cancer Histology Image ClassificationBreast Cancer DetectionMedical DiagnosisClassification Of Breast Cancer Histology ImagesTranslationMedical Image GenerationImage GenerationImage-to-Image Translation
PaperPDFCode(official)

Abstract

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://bupt-ai-cz.github.io/BCI.

Results

TaskDatasetMetricValueModel
Image-to-Image TranslationFLIRPSNR11.14BCI
Image-to-Image TranslationFLIRSSIM0.21BCI
Image-to-Image TranslationBCIAverage PSNR21.16pyramidpix2pix
Image-to-Image TranslationBCISSIM0.477pyramidpix2pix
Image-to-Image TranslationBCIAverage PSNR19.634pix2pixHD
Image-to-Image TranslationBCISSIM0.471pix2pixHD
Image-to-Image TranslationBCIAverage PSNR19.328pix2pix
Image-to-Image TranslationBCISSIM0.44pix2pix
Image-to-Image TranslationBCIAverage PSNR16.203cycleGAN
Image-to-Image TranslationBCISSIM0.373cycleGAN
Image-to-Image TranslationLLVIPPSNR12.191pyramidpix2pix
Image-to-Image TranslationLLVIPSSIM0.278pyramidpix2pix
Image-to-Image TranslationLLVIPPSNR11.22cycleGAN
Image-to-Image TranslationLLVIPSSIM0.214cycleGAN
Image-to-Image TranslationLLVIPPSNR11.156pix2pixHD
Image-to-Image TranslationLLVIPSSIM0.228pix2pixHD
Image GenerationFLIRPSNR11.14BCI
Image GenerationFLIRSSIM0.21BCI
Image GenerationBCIAverage PSNR21.16pyramidpix2pix
Image GenerationBCISSIM0.477pyramidpix2pix
Image GenerationBCIAverage PSNR19.634pix2pixHD
Image GenerationBCISSIM0.471pix2pixHD
Image GenerationBCIAverage PSNR19.328pix2pix
Image GenerationBCISSIM0.44pix2pix
Image GenerationBCIAverage PSNR16.203cycleGAN
Image GenerationBCISSIM0.373cycleGAN
Image GenerationLLVIPPSNR12.191pyramidpix2pix
Image GenerationLLVIPSSIM0.278pyramidpix2pix
Image GenerationLLVIPPSNR11.22cycleGAN
Image GenerationLLVIPSSIM0.214cycleGAN
Image GenerationLLVIPPSNR11.156pix2pixHD
Image GenerationLLVIPSSIM0.228pix2pixHD
1 Image, 2*2 StitchingFLIRPSNR11.14BCI
1 Image, 2*2 StitchingFLIRSSIM0.21BCI
1 Image, 2*2 StitchingBCIAverage PSNR21.16pyramidpix2pix
1 Image, 2*2 StitchingBCISSIM0.477pyramidpix2pix
1 Image, 2*2 StitchingBCIAverage PSNR19.634pix2pixHD
1 Image, 2*2 StitchingBCISSIM0.471pix2pixHD
1 Image, 2*2 StitchingBCIAverage PSNR19.328pix2pix
1 Image, 2*2 StitchingBCISSIM0.44pix2pix
1 Image, 2*2 StitchingBCIAverage PSNR16.203cycleGAN
1 Image, 2*2 StitchingBCISSIM0.373cycleGAN
1 Image, 2*2 StitchingLLVIPPSNR12.191pyramidpix2pix
1 Image, 2*2 StitchingLLVIPSSIM0.278pyramidpix2pix
1 Image, 2*2 StitchingLLVIPPSNR11.22cycleGAN
1 Image, 2*2 StitchingLLVIPSSIM0.214cycleGAN
1 Image, 2*2 StitchingLLVIPPSNR11.156pix2pixHD
1 Image, 2*2 StitchingLLVIPSSIM0.228pix2pixHD

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