Abstract
The gold standard for diagnosing cancer is through pathological examination. This typically involves the utilization of staining techniques such as hematoxylin-eosin (H &E) and immunohistochemistry (IHC) as relying solely on H &E can sometimes result in inaccurate cancer diagnoses. IHC examination offers additional evidence to support the diagnostic process. Given challenging accessibility issues of IHC examination, generating virtual IHC images from H &E-stained images presents a viable solution. This study proposes Active Medical Segmentation and Rendering (AMSR), an end-to-end framework for biomarker expression levels prediction and virtual staining, leveraging constrained Generative Adversarial Networks (GAN). The proposed framework mimics the staining processes, surpassing prior works and offering a feasible substitute for traditional histopathology methods. Preliminary results are presented using a clinical trial dataset pertaining to the CEACAM5 biomarker.
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Zhao, W. et al. (2024). Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_1
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