# DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides

arXiv: https://arxiv.org/abs/2204.04494 · HTML: https://mlsanity.com/paper/2204.04494/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Built on by (most cited first, 9 of 9)

- [StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining](https://mlsanity.com/paper/2403.11340/) (2024)
- [Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon](https://mlsanity.com/paper/2308.13182/) (2023)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [DEPAS: De-novo Pathology Semantic Masks using a Generative Model](https://mlsanity.com/paper/2302.06513/) (2023)
- [An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment](https://mlsanity.com/paper/2305.16465/) (2023)
- [DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation](https://mlsanity.com/paper/2309.00188/) (2023)
- [PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry](https://mlsanity.com/paper/2601.16024/) (2026)
- [Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translation](https://mlsanity.com/paper/2603.16160/) (2026)
- [CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space](https://mlsanity.com/paper/2606.00472/) (2026)

## Abstract (arXiv)

In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.