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Data · dataset · 2026

NSCLC-HEMIF: A large-scale paired H&E and multiplex immunofluorescence image dataset for virtual staining of the non-small cell lung cancer microenvironment

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NSCLC-HEMIF: A paired H&E and multiplex immunofluorescence image datasetNSCLC-HEMIF is a curated paired histopathology image dataset developed for virtual staining and cross-modal computational pathology research in the non-small cell lung cancer microenvironment.

Description

The dataset was generated from adjacent serial tissue sections stained with hematoxylin and eosin (H&E) and multiplex immunofluorescence (mIF). The mIF panel includes the nuclear counterstain DAPI and five immune markers: CD4, CD20, CD38, CD66B and FOXP3.The released dataset contains 10,000 coordinate-matched patch sets, each with a spatial resolution of 270 × 270 pixels.

Each patch set comprises eight JPG images: one H&E patch, one mIF composite patch, one DAPI patch and five marker-specific patches corresponding to CD4, CD20, CD38, CD66B and FOXP3. The files are organized into eight modality-specific folders, and identically named files across these folders represent the same tissue region from the same WSI-level chip and spatial coordinate. An accompanying metadata spreadsheet links each retained patch to its patient- and slide-level information.The data-generation workflow included whole-slide image registration, spectral unmixing, marker-wise morphological denoising, coordinate-matched patch extraction, automated effective-signal filtering using a 1.0% threshold and expert pathology-guided curation.

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Technical validation assessed patient-level patch distribution, H&E–mIF structural correspondence using normalized mutual information, hematoxylin–DAPI nuclear-pattern consistency and marker-wise fluorescence signal-to-noise ratios.NSCLC-HEMIF is intended to support the development and benchmarking of virtual staining, image-to-image translation and spatial immune-marker prediction methods. The paired H&E, mIF composite, DAPI and marker-specific channels may also support studies of image registration, nuclear-pattern analysis and multimodal representation learning.

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Inferred from text
Cancer 75% · Image 75% · Oncology and carcinogenesis 74%
Provenance · 1 source records, 14 field assertions
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ScienceDB10.57760/sciencedb.445637 d agoJSON v1
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