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ImplicitStainer: Data-Efficient Medical Image Translation for Virtual Antibody-based Tissue Staining Using Local Implicit Functions

Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian

TL;DR

The paper tackles the challenge of generating immunohistochemical (IHC) stains from H&E images in a data-efficient manner. It introduces ImplicitStainer, a pixel-level I2I translation model that uses local implicit functions to map pixel neighborhoods to target IHC values, integrated with a dual-encoder backbone that preserves local and global tissue context. On two virtual staining tasks (H&E→CD3 and H&E→CK8/18), it outperforms more than 15 state-of-the-art GAN and diffusion-based methods in PSNR and SSIM, with robust performance under reduced training data. The approach holds potential for accelerating molecular pathology workflows and could be extended to other multi-modal medical image translation tasks and multi-resolution representations.

Abstract

Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology. However, H&E staining does not capture all the diagnostic information that may be needed. To obtain additional molecular information, immunohistochemical (IHC) stains highlight proteins that mark specific cell types, such as CD3 for T-cells or CK8/18 for epithelial cells. While IHC stains are vital for prognosis and treatment guidance, they are typically only available at specialized centers and time consuming to acquire, leading to treatment delays for patients. Virtual staining, enabled by deep learning-based image translation models, provides a promising alternative by computationally generating IHC stains from H&E stained images. Although many GAN and diffusion based image to image (I2I) translation methods have been used for virtual staining, these models treat image patches as independent data points, which results in increased and more diverse data requirements for effective generation. We present ImplicitStainer, a novel approach that leverages local implicit functions to improve image translation, specifically virtual staining performance, by focusing on pixel-level predictions. This method enhances robustness to variations in dataset sizes, delivering high-quality results even with limited data. We validate our approach on two datasets using a comprehensive set of metrics and benchmark it against over fifteen state-of-the-art GAN- and diffusion based models. Full Code and models trained will be released publicly via Github upon acceptance.

ImplicitStainer: Data-Efficient Medical Image Translation for Virtual Antibody-based Tissue Staining Using Local Implicit Functions

TL;DR

The paper tackles the challenge of generating immunohistochemical (IHC) stains from H&E images in a data-efficient manner. It introduces ImplicitStainer, a pixel-level I2I translation model that uses local implicit functions to map pixel neighborhoods to target IHC values, integrated with a dual-encoder backbone that preserves local and global tissue context. On two virtual staining tasks (H&E→CD3 and H&E→CK8/18), it outperforms more than 15 state-of-the-art GAN and diffusion-based methods in PSNR and SSIM, with robust performance under reduced training data. The approach holds potential for accelerating molecular pathology workflows and could be extended to other multi-modal medical image translation tasks and multi-resolution representations.

Abstract

Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology. However, H&E staining does not capture all the diagnostic information that may be needed. To obtain additional molecular information, immunohistochemical (IHC) stains highlight proteins that mark specific cell types, such as CD3 for T-cells or CK8/18 for epithelial cells. While IHC stains are vital for prognosis and treatment guidance, they are typically only available at specialized centers and time consuming to acquire, leading to treatment delays for patients. Virtual staining, enabled by deep learning-based image translation models, provides a promising alternative by computationally generating IHC stains from H&E stained images. Although many GAN and diffusion based image to image (I2I) translation methods have been used for virtual staining, these models treat image patches as independent data points, which results in increased and more diverse data requirements for effective generation. We present ImplicitStainer, a novel approach that leverages local implicit functions to improve image translation, specifically virtual staining performance, by focusing on pixel-level predictions. This method enhances robustness to variations in dataset sizes, delivering high-quality results even with limited data. We validate our approach on two datasets using a comprehensive set of metrics and benchmark it against over fifteen state-of-the-art GAN- and diffusion based models. Full Code and models trained will be released publicly via Github upon acceptance.
Paper Structure (5 sections, 3 equations, 2 figures, 2 tables)

This paper contains 5 sections, 3 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. ImplicitStainer Architecture
  3. Experimental Setup
  4. Results and Discussion
  5. Conclusion and Future Work

Figures (2)

  • Figure 1: ImplicitStainer Architecture. The proposed model combines convolutional and transformer backbones to learn pixel-wise representations, balancing local and global features. The implicit model block uses linear layers with ReLU activation.
  • Figure 2: Qualitative Results Qualitative Results comparing the best performing paried and Unpaired models with ImplicitStainer for both CD3 and CK818.