DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 0 of its 9 references that are in the map. First posted on arXiv 9 Apr 2022.
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Citing papers per year: 2023: 5; 2024: 1; 2025: 0; 2026: 3.
9 papers.
- 2024StainDiffuser: MultiTask Dual Diffusion Model for Virtual Stainingcited by 11
- 2023Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Coloncited by 10
- 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathologycited by 7
- 2023DEPAS: De-novo Pathology Semantic Masks using a Generative Modelcited by 2
- 2023An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironmentcited by 2
- 2023DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentationcited by 0
- 2026PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistrycited by 0
- 2026Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translationcited by 0
- 2026CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action spacecited by 0
- 2026CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space30 May 2026
- 2026Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translation17 Mar 2026
- 2026PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry22 Jan 2026
- 2024StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining17 Mar 2024
- 2023DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation1 Sep 2023
- 2023Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon25 Aug 2023
- 2023An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment25 May 2023
- 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathology5 Apr 2023
- 2023DEPAS: De-novo Pathology Semantic Masks using a Generative Model13 Feb 2023