We propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned pathology image of a sample, ...

We convert a digitally scanned pathology image of a sample, stained using one technique, into another image with a different simulated stain. The challenge is ...

May 1, 2019 · In this work, we propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned ...

In this work, we propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned pathology image of ...

The stain transformation method in computational staining techniques provides doctors with additional diagnostic information beyond H&E images without ...

Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in ...

Jul 5, 2023 · In digital pathology, image properties such as color, brightness, contrast and blurriness may vary based on the scanner and sample preparation.

We compared the accuracy of numerical image registration in re-stained and consecutive sections in histopathology. The median landmark error in re-stained ...

Nov 8, 2022 · Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques.

We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human ...