Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs

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.