Single color digital H&E staining with In-and-Out Net
Mengkun Chen, Yen-Tung Liu, Fadeel Sher Khan, Matthew C. Fox, Jason S., Reichenberg, Fabiana C.P.S. Lopes, Katherine R. Sebastian, Mia K. Markey and, James W. Tunnell

TL;DR
This paper presents In-and-Out Net, a GAN-based model for virtual H&E staining of RCM images, achieving state-of-the-art results without registration, and enhancing tissue analysis efficiency.
Contribution
Introduces a novel GAN-based network for virtual H&E staining that simplifies training and improves accuracy over existing methods.
Findings
Achieves state-of-the-art virtual staining performance.
Eliminates the need for image registration in training.
Provides a validated, efficient virtual staining tool for histological analysis.
Abstract
Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
